IU Mindset Data Analyses
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## steplibrary(modEvA)
library(report)
library(rsconnect)Set up
Full_data_all_r <- read_csv("MI_Data_B1W1M1.csv") %>%
mutate(B_FI_1r = B_FI_friends-1) %>%
mutate(B_FI_2r = B_FI_strangers-1) %>%
mutate(B_FI_3r = B_FI_work-1) %>%
mutate(B_FI_4r = B_FI_education-1) %>%
mutate(B_FI_5r = B_FI_hobbies-1) %>%
mutate(B_PHQ_1r = B_PHQ_1-1) %>%
mutate(B_PHQ_2r = B_PHQ_2-1) %>%
mutate(B_PHQ_3r = B_PHQ_3-1) %>%
mutate(B_PHQ_4r = B_PHQ_4-1) %>%
mutate(B_PHQ_5r = B_PHQ_5-1) %>%
mutate(B_PHQ_6r = B_PHQ_6-1) %>%
mutate(B_PHQ_7r = B_PHQ_7-1) %>%
mutate(B_PHQ_8r = B_PHQ_8-1) %>%
mutate(B_GAD_1r = B_GAD_1-1) %>%
mutate(B_GAD_2r = B_GAD_2-1) %>%
mutate(B_GAD_3r = B_GAD_3-1) %>%
mutate(B_GAD_4r = B_GAD_4-1) %>%
mutate(B_GAD_5r = B_GAD_5-1) %>%
mutate(B_GAD_6r = B_GAD_6-1) %>%
mutate(B_GAD_7r = B_GAD_7-1) %>%
mutate(W1_FI_1r = W1_FI_friends-1) %>%
mutate(W1_FI_2r = W1_FI_strangers-1) %>%
mutate(W1_FI_3r = W1_FI_work-1) %>%
mutate(W1_FI_4r = W1_FI_education-1) %>%
mutate(W1_FI_5r = W1_FI_hobbies-1) %>%
mutate(W1_PHQ_1r = W1_PHQ_1-1) %>%
mutate(W1_PHQ_2r = W1_PHQ_2-1) %>%
mutate(W1_PHQ_3r = W1_PHQ_3-1) %>%
mutate(W1_PHQ_4r = W1_PHQ_4-1) %>%
mutate(W1_PHQ_5r = W1_PHQ_5-1) %>%
mutate(W1_PHQ_6r = W1_PHQ_6-1) %>%
mutate(W1_PHQ_7r = W1_PHQ_7-1) %>%
mutate(W1_PHQ_8r = W1_PHQ_8-1) %>%
mutate(W1_GAD_1r = W1_GAD_1-1) %>%
mutate(W1_GAD_2r = W1_GAD_2-1) %>%
mutate(W1_GAD_3r = W1_GAD_3-1) %>%
mutate(W1_GAD_4r = W1_GAD_4-1) %>%
mutate(W1_GAD_5r = W1_GAD_5-1) %>%
mutate(W1_GAD_6r = W1_GAD_6-1) %>%
mutate(W1_GAD_7r = W1_GAD_7-1) %>%
mutate(M1_FI_1r = M1_FI_friends-1) %>%
mutate(M1_FI_2r = M1_FI_strangers-1) %>%
mutate(M1_FI_3r = M1_FI_work-1) %>%
mutate(M1_FI_4r = M1_FI_education-1) %>%
mutate(M1_FI_5r = M1_FI_hobbies-1) %>%
mutate(M1_PHQ_1r = M1_PHQ_1-1) %>%
mutate(M1_PHQ_2r = M1_PHQ_2-1) %>%
mutate(M1_PHQ_3r = M1_PHQ_3-1) %>%
mutate(M1_PHQ_4r = M1_PHQ_4-1) %>%
mutate(M1_PHQ_5r = M1_PHQ_5-1) %>%
mutate(M1_PHQ_6r = M1_PHQ_6-1) %>%
mutate(M1_PHQ_7r = M1_PHQ_7-1) %>%
mutate(M1_PHQ_8r = M1_PHQ_8-1) %>%
mutate(M1_GAD_1r = M1_GAD_1-1) %>%
mutate(M1_GAD_2r = M1_GAD_2-1) %>%
mutate(M1_GAD_3r = M1_GAD_3-1) %>%
mutate(M1_GAD_4r = M1_GAD_4-1) %>%
mutate(M1_GAD_5r = M1_GAD_5-1) %>%
mutate(M1_GAD_6r = M1_GAD_6-1) %>%
mutate(M1_GAD_7r = M1_GAD_7-1)## New names:
## Rows: 259 Columns: 189
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Prolific_ID, Group dbl (187): ...1, ID, B_IUS_1, B_IUS_2, B_IUS_3,
## B_IUS_4, B_IUS_5, B_IUS_6, B...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`Full_data_all_r1 <- read.csv("Full_data_all_r.csv")Reading the full data (intervention group, psychoeducation control group, and EC group) and calculating total scores
Full_data_all_f <- Full_data_all_r1 %>%
rowwise() %>%
mutate(A_PRE_IUS_total = sum(B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5, B_IUS_6, B_IUS_7, B_IUS_8, B_IUS_9, B_IUS_10, B_IUS_11, B_IUS_12, na.rm = TRUE)) %>%
mutate(A_PRE_FI_total = sum(B_FI_1r, B_FI_2r, B_FI_3r, B_FI_4r, B_FI_5r)) %>%
mutate(A_PRE_RTQ_total = sum(B_RTQ_1, B_RTQ_2, B_RTQ_3, B_RTQ_4, B_RTQ_5, B_RTQ_6, B_RTQ_7, B_RTQ_8, B_RTQ_9, B_RTQ_10, na.rm = TRUE)) %>%
mutate(A_PRE_ERQ_Rtotal = sum(B_ERQ_1, B_ERQ_3, B_ERQ_5, B_ERQ_7, B_ERQ_8, B_ERQ_10, na.rm = TRUE)) %>%
mutate(A_PRE_PHQ_total = sum(B_PHQ_1r, B_PHQ_2r, B_PHQ_3r, B_PHQ_4r, B_PHQ_5r, B_PHQ_6r, B_PHQ_7r, B_PHQ_8r)) %>%
mutate(A_PRE_GAD_total = sum(B_GAD_1r, B_GAD_2r, B_GAD_3r, B_GAD_4r, B_GAD_5r, B_GAD_6r, B_GAD_7r)) %>%
mutate(B_POST_IUS_total = sum(POST_IUS_1, POST_IUS_2, POST_IUS_3, POST_IUS_4, POST_IUS_5, POST_IUS_6, POST_IUS_7, POST_IUS_8, POST_IUS_9, POST_IUS_10, POST_IUS_11, POST_IUS_12, na.rm = TRUE)) %>%
mutate(C_W1_IUS_total = sum(W1_IUS_1, W1_IUS_2, W1_IUS_3, W1_IUS_4, W1_IUS_5, W1_IUS_6, W1_IUS_7, W1_IUS_8, W1_IUS_9, W1_IUS_10, W1_IUS_11, W1_IUS_12, na.rm = TRUE)) %>%
mutate(C_W1_FI_total = sum(W1_FI_1r, W1_FI_2r, W1_FI_3r, W1_FI_4r, W1_FI_5r)) %>%
mutate(C_W1_RTQ_total = sum(W1_RTQ_1, W1_RTQ_2, W1_RTQ_3, W1_RTQ_4, W1_RTQ_5, W1_RTQ_6, W1_RTQ_7, W1_RTQ_8, W1_RTQ_9, W1_RTQ_10, na.rm = TRUE)) %>%
mutate(C_W1_ERQ_Rtotal = sum(W1_ERQ_1, W1_ERQ_3, W1_ERQ_5, W1_ERQ_7, W1_ERQ_8, W1_ERQ_10, na.rm = TRUE)) %>%
mutate(C_W1_PHQ_total = sum(W1_PHQ_1r, W1_PHQ_2r, W1_PHQ_3r, W1_PHQ_4r, W1_PHQ_5r, W1_PHQ_6r, W1_PHQ_7r, W1_PHQ_8r)) %>%
mutate(C_W1_GAD_total = sum(W1_GAD_1r, W1_GAD_2r, W1_GAD_3r, W1_GAD_4r, W1_GAD_5r, W1_GAD_6r, W1_GAD_7r)) %>%
mutate(D_M1_IUS_total = sum(M1_IUS_1, M1_IUS_2, M1_IUS_3, M1_IUS_4, M1_IUS_5, M1_IUS_6, M1_IUS_7, M1_IUS_8, M1_IUS_9, M1_IUS_10, M1_IUS_11, M1_IUS_12, na.rm = TRUE)) %>%
mutate(D_M1_FI_total = sum(M1_FI_1r, M1_FI_2r, M1_FI_3r, M1_FI_4r, M1_FI_5r)) %>%
mutate(D_M1_RTQ_total = sum(M1_RTQ_1, M1_RTQ_2, M1_RTQ_3, M1_RTQ_4, M1_RTQ_5, M1_RTQ_6, M1_RTQ_7, M1_RTQ_8, M1_RTQ_9, M1_RTQ_10, na.rm = TRUE)) %>%
mutate(D_M1_ERQ_Rtotal = sum(M1_ERQ_1, M1_ERQ_3, M1_ERQ_5, M1_ERQ_7, M1_ERQ_8, M1_ERQ_10, na.rm = TRUE)) %>%
mutate(D_M1_PHQ_total = sum(M1_PHQ_1r, M1_PHQ_2r, M1_PHQ_3r, M1_PHQ_4r, M1_PHQ_5r, M1_PHQ_6r, M1_PHQ_7r, M1_PHQ_8r)) %>%
mutate(D_M1_GAD_total = sum(M1_GAD_1r, M1_GAD_2r, M1_GAD_3r, M1_GAD_4r, M1_GAD_5r, M1_GAD_6r, M1_GAD_7r)) %>%
ungroup()# People who had not completed a follow-up had total scores of 0 rather than NA - so here I give them NA
Full_data_all_t <- Full_data_all_f %>%
mutate_at(c('A_PRE_IUS_total', 'B_POST_IUS_total', 'C_W1_IUS_total', 'D_M1_IUS_total'), ~na_if(., 0)) %>%
mutate_at(c('A_PRE_RTQ_total', 'C_W1_RTQ_total', 'D_M1_RTQ_total'), ~na_if(., 0)) %>%
mutate_at(c('A_PRE_ERQ_Rtotal', 'C_W1_ERQ_Rtotal', 'D_M1_ERQ_Rtotal'), ~na_if(., 0))Calculating mood means
Full_data_all <- mutate(Full_data_all_t, A_PRE_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(B_distressed_pleasant, B_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, B_POST_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(POST_distressed_pleasant, POST_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, C_W1_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(W1_distressed_pleasant, W1_anxious_relaxed)), na.rm = TRUE)) %>%
mutate(Full_data_all_t, D_M1_mood_mean = rowMeans(dplyr::select(Full_data_all_t, c(M1_distressed_pleasant, M1_anxious_relaxed)), na.rm = TRUE))write.csv(Full_data_all, "Full_data_all.csv")Hypothesis 1
H1a: IUS and Mood association at PRE
#Distressed
PRE_IUS_Distress_lm <- lm(A_PRE_IUS_total ~ B_distressed_pleasant, data = Full_data_all)
summary(PRE_IUS_Distress_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_distressed_pleasant, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.8365 -5.8426 0.4123 6.3128 20.1076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.3702 0.7218 61.471 < 2e-16 ***
## B_distressed_pleasant -0.0559 0.0129 -4.334 2.11e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.571 on 257 degrees of freedom
## Multiple R-squared: 0.0681, Adjusted R-squared: 0.06448
## F-statistic: 18.78 on 1 and 257 DF, p-value: 2.105e-05anova(PRE_IUS_Distress_lm) %>%
report()## The ANOVA suggests that:
##
## - The main effect of B_distressed_pleasant is statistically significant and
## medium (F(1, 257) = 18.78, p < .001; Eta2 = 0.07, 95% CI [0.03, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.#Anxious
PRE_IUS_Anxiety_lm <- lm(A_PRE_IUS_total ~ B_anxious_relaxed, data = Full_data_all)
summary(PRE_IUS_Anxiety_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_anxious_relaxed, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.2289 -5.7679 0.5456 5.9105 20.4901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.00514 0.61670 71.355 < 2e-16 ***
## B_anxious_relaxed -0.05551 0.01021 -5.437 1.26e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.416 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1035, Adjusted R-squared: 0.1
## F-statistic: 29.56 on 1 and 256 DF, p-value: 1.262e-07anova(PRE_IUS_Anxiety_lm) %>%
report()## The ANOVA suggests that:
##
## - The main effect of B_anxious_relaxed is statistically significant and medium
## (F(1, 256) = 29.56, p < .001; Eta2 = 0.10, 95% CI [0.05, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.#Combined
PRE_IUS_mood_lm <- lm(A_PRE_IUS_total ~ A_PRE_mood_mean, data = Full_data_all)
summary(PRE_IUS_mood_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_mood_mean, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.2840 -6.0303 0.1752 6.0332 20.9727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.57405 0.67547 65.989 < 2e-16 ***
## A_PRE_mood_mean -0.06646 0.01228 -5.412 1.43e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.412 on 257 degrees of freedom
## Multiple R-squared: 0.1023, Adjusted R-squared: 0.0988
## F-statistic: 29.29 on 1 and 257 DF, p-value: 1.431e-07anova(PRE_IUS_mood_lm) %>%
report()## The ANOVA suggests that:
##
## - The main effect of A_PRE_mood_mean is statistically significant and medium
## (F(1, 257) = 29.29, p < .001; Eta2 = 0.10, 95% CI [0.05, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.H1a: IUS and mental health association at PRE
#Depression
PRE_IUS_PHQ_lm <- lm(A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
summary(PRE_IUS_PHQ_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.9576 -4.5129 0.0901 4.9155 22.1617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.4890 0.9776 36.303 < 2e-16 ***
## A_PRE_PHQ_total 0.6746 0.0841 8.021 3.72e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.94 on 257 degrees of freedom
## Multiple R-squared: 0.2002, Adjusted R-squared: 0.1971
## F-statistic: 64.34 on 1 and 257 DF, p-value: 3.716e-14anova(PRE_IUS_PHQ_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_PHQ_total 1 4056.3 4056.3 64.344 3.716e-14 ***
## Residuals 257 16201.4 63.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Anxiety
PRE_IUS_GAD_lm <- lm(A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
summary(PRE_IUS_GAD_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.3228 -4.8228 0.7116 4.1772 20.6429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.04669 0.88936 39.41 <2e-16 ***
## A_PRE_GAD_total 0.82761 0.08639 9.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.621 on 257 degrees of freedom
## Multiple R-squared: 0.2631, Adjusted R-squared: 0.2603
## F-statistic: 91.78 on 1 and 257 DF, p-value: < 2.2e-16anova(PRE_IUS_GAD_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_GAD_total 1 5330.7 5330.7 91.78 < 2.2e-16 ***
## Residuals 257 14926.9 58.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1H1b: IUS and BT at PRE
# Adding in groups + excluding participants who only sampled (never made a choice = did not understand task)
BT_PRE_POST <- merge(BT_full,Full_data_all,
by=c("ID"),
all = TRUE) %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(ID != "8892522", ID != "8892570", ID != "8892628", ID != "8892668", ID != "8892681", ID != "8892779", ID != "8892794", ID != "8893157", ID != "8893186", ID != "8892873", ID != "9113535", ID != "9113549", ID != "9113550") # excluding those not making a choiceKeeping those who never sample (only make choices)
#Analysis
PRE_IUS_BT_lm <- lm(A_PRE_IUS_total ~ A_PRE_samples, data = BT_PRE_POST)
summary(PRE_IUS_BT_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_samples, data = BT_PRE_POST)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.813 -5.889 1.036 6.280 17.187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.81292 0.61535 69.575 < 2e-16 ***
## A_PRE_samples -0.04991 0.01818 -2.745 0.00651 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.754 on 244 degrees of freedom
## Multiple R-squared: 0.02995, Adjusted R-squared: 0.02598
## F-statistic: 7.534 on 1 and 244 DF, p-value: 0.006505anova(PRE_IUS_BT_lm) %>%
report()## The ANOVA suggests that:
##
## - The main effect of A_PRE_samples is statistically significant and small (F(1,
## 244) = 7.53, p = 0.007; Eta2 = 0.03, 95% CI [4.79e-03, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.Excluding those who never sample
BT_removed_PRE <- BT_PRE_POST %>%
filter(A_PRE_samples != "0") # only excluding from PRE #Analysis
PRE_IUS_BT_removed_lm <- lm(A_PRE_IUS_total ~ A_PRE_samples, data = BT_removed_PRE)
summary(PRE_IUS_BT_removed_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_samples, data = BT_removed_PRE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.606 -5.932 1.014 5.934 15.934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.14794 0.72669 59.376 < 2e-16 ***
## A_PRE_samples -0.05408 0.01801 -3.003 0.00307 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.265 on 171 degrees of freedom
## Multiple R-squared: 0.0501, Adjusted R-squared: 0.04454
## F-statistic: 9.018 on 1 and 171 DF, p-value: 0.003074anova(PRE_IUS_BT_removed_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_samples 1 616.1 616.12 9.0184 0.003074 **
## Residuals 171 11682.4 68.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Hypothesis 2
H2a: difference in change in cognitive IUS over time between groups
IUS_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total")## Formatting table as needed
IUS_alltimepoints_long <- IUS_alltimepoints %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_alltimepoints_long, REML = TRUE)
summary(IUS_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_alltimepoints_long
##
## REML criterion at convergence: 6622.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3573 -0.4905 -0.0178 0.4981 4.1217
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 74.25 8.617
## Residual 23.72 4.870
## Number of obs: 997, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3998 373.3956 29.348
## GroupB_Controls 0.9483 1.6981 373.3956 0.558
## GroupC_Intervention 1.9880 1.7060 373.3956 1.165
## TimeB_POST_IUS_total -0.2800 0.9741 729.2358 -0.287
## TimeC_W1_IUS_total 0.9453 0.9872 730.1693 0.958
## TimeD_M1_IUS_total 2.3445 1.0164 732.1184 2.307
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 1.1817 729.2358 -3.036
## GroupC_Intervention:TimeB_POST_IUS_total -6.0722 1.1897 729.3838 -5.104
## GroupB_Controls:TimeC_W1_IUS_total -2.7704 1.1950 730.0826 -2.318
## GroupC_Intervention:TimeC_W1_IUS_total -5.4825 1.2018 730.1602 -4.562
## GroupB_Controls:TimeD_M1_IUS_total -4.0381 1.2321 732.1275 -3.277
## GroupC_Intervention:TimeD_M1_IUS_total -6.8784 1.2383 732.1595 -5.555
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.57687
## GroupC_Intervention 0.24465
## TimeB_POST_IUS_total 0.77384
## TimeC_W1_IUS_total 0.33860
## TimeD_M1_IUS_total 0.02135 *
## GroupB_Controls:TimeB_POST_IUS_total 0.00248 **
## GroupC_Intervention:TimeB_POST_IUS_total 4.24e-07 ***
## GroupB_Controls:TimeC_W1_IUS_total 0.02070 *
## GroupC_Intervention:TimeC_W1_IUS_total 5.95e-06 ***
## GroupB_Controls:TimeD_M1_IUS_total 0.00110 **
## GroupC_Intervention:TimeD_M1_IUS_total 3.89e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS TC_W1_ TD_M1_ GB_C:TB GC_I:TB GB_C:TC
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TB_POST_IUS -0.348 0.287 0.285
## TmC_W1_IUS_ -0.343 0.283 0.282 0.493
## TmD_M1_IUS_ -0.333 0.275 0.274 0.479 0.472
## GB_C:TB_POS 0.287 -0.348 -0.235 -0.824 -0.407 -0.395
## GC_I:TB_POS 0.285 -0.235 -0.347 -0.819 -0.404 -0.392 0.675
## GB_C:TC_W1_ 0.284 -0.344 -0.233 -0.408 -0.826 -0.390 0.494 0.334
## GC_I:TC_W1_ 0.282 -0.232 -0.344 -0.405 -0.821 -0.388 0.334 0.493 0.679
## GB_C:TD_M1_ 0.275 -0.334 -0.226 -0.395 -0.389 -0.825 0.480 0.324 0.474
## GC_I:TD_M1_ 0.274 -0.226 -0.334 -0.393 -0.387 -0.821 0.324 0.478 0.320
## GC_I:TC GB_C:TD
## GrpB_Cntrls
## GrpC_Intrvn
## TB_POST_IUS
## TmC_W1_IUS_
## TmD_M1_IUS_
## GB_C:TB_POS
## GC_I:TB_POS
## GB_C:TC_W1_
## GC_I:TC_W1_
## GB_C:TD_M1_ 0.320
## GC_I:TD_M1_ 0.473 0.677anova (IUS_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 68.32 34.16 2 256.33 1.4401 0.2388
## Time 1437.93 479.31 3 731.09 20.2071 1.371e-12 ***
## Group:Time 983.96 163.99 6 731.08 6.9138 3.702e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.33 – 43.83 | <0.001 |
| Group [B_Controls] | 0.95 | -2.38 – 4.28 | 0.577 |
| Group [C_Intervention] | 1.99 | -1.36 – 5.34 | 0.244 |
| Time [B_POST_IUS_total] | -0.28 | -2.19 – 1.63 | 0.774 |
| Time [C_W1_IUS_total] | 0.95 | -0.99 – 2.88 | 0.339 |
| Time [D_M1_IUS_total] | 2.34 | 0.35 – 4.34 | 0.021 |
|
Group [B_Controls] × Time [B_POST_IUS_total] |
-3.59 | -5.91 – -1.27 | 0.002 |
|
Group [C_Intervention] × Time [B_POST_IUS_total] |
-6.07 | -8.41 – -3.74 | <0.001 |
|
Group [B_Controls] × Time [C_W1_IUS_total] |
-2.77 | -5.12 – -0.43 | 0.021 |
|
Group [C_Intervention] × Time [C_W1_IUS_total] |
-5.48 | -7.84 – -3.12 | <0.001 |
|
Group [B_Controls] × Time [D_M1_IUS_total] |
-4.04 | -6.46 – -1.62 | 0.001 |
|
Group [C_Intervention] × Time [D_M1_IUS_total] |
-6.88 | -9.31 – -4.45 | <0.001 |
| Random Effects | |||
| σ2 | 23.72 | ||
| Ï„00 ID | 74.25 | ||
| ICC | 0.76 | ||
| N ID | 259 | ||
| Observations | 997 | ||
| Marginal R2 / Conditional R2 | 0.040 / 0.768 | ||
parameters::standardise_parameters(IUS_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.37]
## GroupB_Controls | 0.09 | [-0.24, 0.43]
## GroupC_Intervention | 0.20 | [-0.14, 0.53]
## TimeB_POST_IUS_total | -0.03 | [-0.22, 0.16]
## TimeC_W1_IUS_total | 0.09 | [-0.10, 0.29]
## TimeD_M1_IUS_total | 0.23 | [ 0.03, 0.43]
## GroupB_Controls:TimeB_POST_IUS_total | -0.36 | [-0.59, -0.13]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.60 | [-0.84, -0.37]
## GroupB_Controls:TimeC_W1_IUS_total | -0.28 | [-0.51, -0.04]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.55 | [-0.78, -0.31]
## GroupB_Controls:TimeD_M1_IUS_total | -0.40 | [-0.64, -0.16]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.68 | [-0.93, -0.44]report(IUS_MEM)## We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)
## to predict IUS_Score with Group and Time (formula: IUS_Score ~ Group * Time).
## The model included ID as random effect (formula: ~1 | ID). The model's total
## explanatory power is substantial (conditional R2 = 0.77) and the part related
## to the fixed effects alone (marginal R2) is of 0.04. The model's intercept,
## corresponding to Group = A_ECs and Time = A_PRE_IUS_total, is at 41.08 (95% CI
## [38.33, 43.83], t(983) = 29.35, p < .001). Within this model:
##
## - The effect of Group [B_Controls] is statistically non-significant and
## positive (beta = 0.95, 95% CI [-2.38, 4.28], t(983) = 0.56, p = 0.577; Std.
## beta = 0.09, 95% CI [-0.24, 0.43])
## - The effect of Group [C_Intervention] is statistically non-significant and
## positive (beta = 1.99, 95% CI [-1.36, 5.34], t(983) = 1.17, p = 0.244; Std.
## beta = 0.20, 95% CI [-0.14, 0.53])
## - The effect of Time [B_POST_IUS_total] is statistically non-significant and
## negative (beta = -0.28, 95% CI [-2.19, 1.63], t(983) = -0.29, p = 0.774; Std.
## beta = -0.03, 95% CI [-0.22, 0.16])
## - The effect of Time [C_W1_IUS_total] is statistically non-significant and
## positive (beta = 0.95, 95% CI [-0.99, 2.88], t(983) = 0.96, p = 0.339; Std.
## beta = 0.09, 95% CI [-0.10, 0.29])
## - The effect of Time [D_M1_IUS_total] is statistically significant and positive
## (beta = 2.34, 95% CI [0.35, 4.34], t(983) = 2.31, p = 0.021; Std. beta = 0.23,
## 95% CI [0.03, 0.43])
## - The effect of Group [B_Controls] × Time [B_POST_IUS_total] is statistically
## significant and negative (beta = -3.59, 95% CI [-5.91, -1.27], t(983) = -3.04,
## p = 0.002; Std. beta = -0.36, 95% CI [-0.59, -0.13])
## - The effect of Group [C_Intervention] × Time [B_POST_IUS_total] is
## statistically significant and negative (beta = -6.07, 95% CI [-8.41, -3.74],
## t(983) = -5.10, p < .001; Std. beta = -0.60, 95% CI [-0.84, -0.37])
## - The effect of Group [B_Controls] × Time [C_W1_IUS_total] is statistically
## significant and negative (beta = -2.77, 95% CI [-5.12, -0.43], t(983) = -2.32,
## p = 0.021; Std. beta = -0.28, 95% CI [-0.51, -0.04])
## - The effect of Group [C_Intervention] × Time [C_W1_IUS_total] is statistically
## significant and negative (beta = -5.48, 95% CI [-7.84, -3.12], t(983) = -4.56,
## p < .001; Std. beta = -0.55, 95% CI [-0.78, -0.31])
## - The effect of Group [B_Controls] × Time [D_M1_IUS_total] is statistically
## significant and negative (beta = -4.04, 95% CI [-6.46, -1.62], t(983) = -3.28,
## p = 0.001; Std. beta = -0.40, 95% CI [-0.64, -0.16])
## - The effect of Group [C_Intervention] × Time [D_M1_IUS_total] is statistically
## significant and negative (beta = -6.88, 95% CI [-9.31, -4.45], t(983) = -5.55,
## p < .001; Std. beta = -0.68, 95% CI [-0.93, -0.44])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald t-distribution approximation.Effect of time in the intervention group
IUS_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
IUS_I_long <- IUS_I %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_I <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_I_long, REML = TRUE)
summary(IUS_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_I_long
##
## REML criterion at convergence: 2660.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6094 -0.5072 0.0191 0.4921 3.9112
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 76.66 8.756
## Residual 26.24 5.123
## Number of obs: 395, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.0680 0.9995 151.9487 43.088 < 2e-16 ***
## TimeB_POST_IUS_total -6.3517 0.7184 289.3895 -8.841 < 2e-16 ***
## TimeC_W1_IUS_total -4.5354 0.7210 289.5807 -6.290 1.17e-09 ***
## TimeD_M1_IUS_total -4.5350 0.7440 290.4668 -6.095 3.47e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TB_POS TC_W1_
## TB_POST_IUS -0.355
## TmC_W1_IUS_ -0.354 0.492
## TmD_M1_IUS_ -0.343 0.476 0.477anova (IUS_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 2242.3 747.43 3 290.06 28.481 3.601e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_I)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 43.07 | 41.10 – 45.03 | <0.001 |
| Time [B_POST_IUS_total] | -6.35 | -7.76 – -4.94 | <0.001 |
| Time [C_W1_IUS_total] | -4.54 | -5.95 – -3.12 | <0.001 |
| Time [D_M1_IUS_total] | -4.54 | -6.00 – -3.07 | <0.001 |
| Random Effects | |||
| σ2 | 26.24 | ||
| Ï„00 ID | 76.66 | ||
| ICC | 0.74 | ||
| N ID | 103 | ||
| Observations | 395 | ||
| Marginal R2 / Conditional R2 | 0.053 / 0.758 | ||
parameters::standardise_parameters(IUS_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.36 | [ 0.17, 0.55]
## TimeB_POST_IUS_total | -0.61 | [-0.75, -0.47]
## TimeC_W1_IUS_total | -0.44 | [-0.57, -0.30]
## TimeD_M1_IUS_total | -0.44 | [-0.58, -0.30]Effect of time in the control group
IUS_C <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
IUS_C_long <- IUS_C %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_C <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_C_long, REML = TRUE)
summary(IUS_MEM_C)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_C_long
##
## REML criterion at convergence: 2762.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1699 -0.4765 -0.0261 0.4975 2.9038
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 81.89 9.050
## Residual 25.89 5.088
## Number of obs: 410, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.0283 1.0084 152.6590 41.680 < 2e-16 ***
## TimeB_POST_IUS_total -3.8679 0.6989 300.9847 -5.535 6.79e-08 ***
## TimeC_W1_IUS_total -1.8251 0.7034 301.2551 -2.595 0.00993 **
## TimeD_M1_IUS_total -1.6938 0.7275 302.1758 -2.328 0.02057 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TB_POS TC_W1_
## TB_POST_IUS -0.347
## TmC_W1_IUS_ -0.344 0.497
## TmD_M1_IUS_ -0.333 0.480 0.479anova (IUS_MEM_C)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 796.69 265.56 3 301.64 10.259 1.886e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_C)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.05 – 44.01 | <0.001 |
| Time [B_POST_IUS_total] | -3.87 | -5.24 – -2.49 | <0.001 |
| Time [C_W1_IUS_total] | -1.83 | -3.21 – -0.44 | 0.010 |
| Time [D_M1_IUS_total] | -1.69 | -3.12 – -0.26 | 0.020 |
| Random Effects | |||
| σ2 | 25.89 | ||
| Ï„00 ID | 81.89 | ||
| ICC | 0.76 | ||
| N ID | 106 | ||
| Observations | 410 | ||
| Marginal R2 / Conditional R2 | 0.018 / 0.764 | ||
parameters::standardise_parameters(IUS_MEM_C)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.17 | [-0.02, 0.36]
## TimeB_POST_IUS_total | -0.37 | [-0.50, -0.24]
## TimeC_W1_IUS_total | -0.17 | [-0.31, -0.04]
## TimeD_M1_IUS_total | -0.16 | [-0.30, -0.03]Effect of time in the EC group
IUS_EC <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
IUS_EC_long <- IUS_EC %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_EC <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_EC_long, REML = TRUE)
summary(IUS_MEM_EC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_EC_long
##
## REML criterion at convergence: 1176.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72869 -0.51486 -0.07816 0.52049 2.97988
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 52.84 7.269
## Residual 13.78 3.713
## Number of obs: 192, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.0800 1.1543 67.5040 35.589 < 2e-16 ***
## TimeB_POST_IUS_total -0.2800 0.7425 139.0560 -0.377 0.70668
## TimeC_W1_IUS_total 0.9450 0.7526 139.2045 1.256 0.21134
## TimeD_M1_IUS_total 2.3452 0.7749 139.5143 3.026 0.00295 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TB_POS TC_W1_
## TB_POST_IUS -0.322
## TmC_W1_IUS_ -0.317 0.493
## TmD_M1_IUS_ -0.308 0.479 0.472anova (IUS_MEM_EC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 190.27 63.422 3 139.36 4.6012 0.004206 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_EC)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.80 – 43.36 | <0.001 |
| Time [B_POST_IUS_total] | -0.28 | -1.74 – 1.18 | 0.707 |
| Time [C_W1_IUS_total] | 0.94 | -0.54 – 2.43 | 0.211 |
| Time [D_M1_IUS_total] | 2.35 | 0.82 – 3.87 | 0.003 |
| Random Effects | |||
| σ2 | 13.78 | ||
| Ï„00 ID | 52.84 | ||
| ICC | 0.79 | ||
| N ID | 50 | ||
| Observations | 192 | ||
| Marginal R2 / Conditional R2 | 0.015 / 0.796 | ||
parameters::standardise_parameters(IUS_MEM_EC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | -0.09 | [-0.36, 0.19]
## TimeB_POST_IUS_total | -0.03 | [-0.21, 0.14]
## TimeC_W1_IUS_total | 0.12 | [-0.07, 0.30]
## TimeD_M1_IUS_total | 0.29 | [ 0.10, 0.47]Baseline to post
IUS_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total")## Formatting table as needed
IUS_BP_long <- IUS_BP %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_BP <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long, REML = TRUE)
summary(IUS_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_BP_long
##
## REML criterion at convergence: 3592.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95161 -0.44757 0.00781 0.41532 2.86841
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 71.13 8.434
## Residual 24.28 4.927
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3814 328.7093 29.738
## GroupB_Controls 0.9483 1.6758 328.7093 0.566
## GroupC_Intervention 1.9880 1.6836 328.7093 1.181
## TimeB_POST_IUS_total -0.2800 0.9854 254.2485 -0.284
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 1.1955 254.2485 -3.001
## GroupC_Intervention:TimeB_POST_IUS_total -6.0437 1.2044 254.6121 -5.018
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.57186
## GroupC_Intervention 0.23854
## TimeB_POST_IUS_total 0.77654
## GroupB_Controls:TimeB_POST_IUS_total 0.00296 **
## GroupC_Intervention:TimeB_POST_IUS_total 9.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TB_POST_IUS -0.357 0.294 0.293
## GB_C:TB_POS 0.294 -0.357 -0.241 -0.824
## GC_I:TB_POS 0.292 -0.241 -0.356 -0.818 0.674anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 10.89 5.45 2 256.08 0.2243 0.7992
## Time 1394.73 1394.73 1 254.53 57.4503 6.459e-13 ***
## Group:Time 617.82 308.91 2 254.58 12.7243 5.407e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_BP)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.37 – 43.79 | <0.001 |
| Group [B_Controls] | 0.95 | -2.34 – 4.24 | 0.572 |
| Group [C_Intervention] | 1.99 | -1.32 – 5.30 | 0.238 |
| Time [B_POST_IUS_total] | -0.28 | -2.22 – 1.66 | 0.776 |
|
Group [B_Controls] × Time [B_POST_IUS_total] |
-3.59 | -5.94 – -1.24 | 0.003 |
|
Group [C_Intervention] × Time [B_POST_IUS_total] |
-6.04 | -8.41 – -3.68 | <0.001 |
| Random Effects | |||
| σ2 | 24.28 | ||
| Ï„00 ID | 71.13 | ||
| ICC | 0.75 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.056 / 0.760 | ||
parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.36]
## GroupB_Controls | 0.09 | [-0.23, 0.42]
## GroupC_Intervention | 0.20 | [-0.13, 0.53]
## TimeB_POST_IUS_total | -0.03 | [-0.22, 0.17]
## GroupB_Controls:TimeB_POST_IUS_total | -0.36 | [-0.59, -0.12]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.60 | [-0.84, -0.37]plot_model(IUS_MEM_BP, type = "int")
Baseline to 1W
IUS_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total")## Formatting table as needed
IUS_B1W_long <- IUS_B1W %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1W <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long, REML = TRUE)
summary(IUS_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1W_long
##
## REML criterion at convergence: 3541.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.05745 -0.40449 -0.00329 0.45694 2.90920
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 64.04 8.003
## Residual 24.69 4.969
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3321 334.4251 30.837
## GroupB_Controls 0.9483 1.6161 334.4251 0.587
## GroupC_Intervention 1.9880 1.6236 334.4251 1.224
## TimeC_W1_IUS_total 0.8991 1.0114 251.5502 0.889
## GroupB_Controls:TimeC_W1_IUS_total -2.7707 1.2233 251.1429 -2.265
## GroupC_Intervention:TimeC_W1_IUS_total -5.4023 1.2307 251.3348 -4.390
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5577
## GroupC_Intervention 0.2217
## TimeC_W1_IUS_total 0.3749
## GroupB_Controls:TimeC_W1_IUS_total 0.0244 *
## GroupC_Intervention:TimeC_W1_IUS_total 1.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_IUS_ -0.366 0.302 0.301
## GB_C:TC_W1_ 0.303 -0.368 -0.249 -0.827
## GC_I:TC_W1_ 0.301 -0.248 -0.367 -0.822 0.679anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.54 2.77 2 256.11 0.1122 0.8938692
## Time 372.30 372.30 1 251.08 15.0803 0.0001318 ***
## Group:Time 499.29 249.64 2 250.97 10.1120 5.976e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1W)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.46 – 43.70 | <0.001 |
| Group [B_Controls] | 0.95 | -2.23 – 4.12 | 0.558 |
| Group [C_Intervention] | 1.99 | -1.20 – 5.18 | 0.221 |
| Time [C_W1_IUS_total] | 0.90 | -1.09 – 2.89 | 0.374 |
|
Group [B_Controls] × Time [C_W1_IUS_total] |
-2.77 | -5.17 – -0.37 | 0.024 |
|
Group [C_Intervention] × Time [C_W1_IUS_total] |
-5.40 | -7.82 – -2.98 | <0.001 |
| Random Effects | |||
| σ2 | 24.69 | ||
| Ï„00 ID | 64.04 | ||
| ICC | 0.72 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.027 / 0.729 | ||
parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -7.24e-03 | [-0.28, 0.27]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.55]
## TimeC_W1_IUS_total | 0.09 | [-0.11, 0.30]
## GroupB_Controls:TimeC_W1_IUS_total | -0.29 | [-0.55, -0.04]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.57 | [-0.82, -0.31]plot_model(IUS_MEM_B1W, type = "int")
Baseline to 1M
IUS_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total")## Formatting table as needed
IUS_B1M_long <- IUS_B1M %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1M <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1M_long, REML = TRUE)
summary(IUS_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1M_long
##
## REML criterion at convergence: 3427.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95787 -0.46559 0.01777 0.50003 2.25579
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.99 7.745
## Residual 29.75 5.454
## Number of obs: 488, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3397 343.4368 30.664
## GroupB_Controls 0.9483 1.6252 343.4368 0.583
## GroupC_Intervention 1.9880 1.6328 343.4368 1.218
## TimeD_M1_IUS_total 2.2079 1.1512 235.9671 1.918
## GroupB_Controls:TimeD_M1_IUS_total -3.9626 1.3951 235.8181 -2.840
## GroupC_Intervention:TimeD_M1_IUS_total -6.9119 1.4023 235.8887 -4.929
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5599
## GroupC_Intervention 0.2242
## TimeD_M1_IUS_total 0.0563 .
## GroupB_Controls:TimeD_M1_IUS_total 0.0049 **
## GroupC_Intervention:TimeD_M1_IUS_total 1.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_IUS_ -0.386 0.318 0.317
## GB_C:TD_M1_ 0.318 -0.386 -0.261 -0.825
## GC_I:TD_M1_ 0.317 -0.261 -0.386 -0.821 0.677anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 28.24 14.12 2 257.23 0.4747 0.622614
## Time 207.73 207.73 1 235.80 6.9828 0.008781 **
## Group:Time 736.40 368.20 2 235.76 12.3767 7.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1M)| Â | IUS Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.45 – 43.71 | <0.001 |
| Group [B_Controls] | 0.95 | -2.25 – 4.14 | 0.560 |
| Group [C_Intervention] | 1.99 | -1.22 – 5.20 | 0.224 |
| Time [D_M1_IUS_total] | 2.21 | -0.05 – 4.47 | 0.056 |
|
Group [B_Controls] × Time [D_M1_IUS_total] |
-3.96 | -6.70 – -1.22 | 0.005 |
|
Group [C_Intervention] × Time [D_M1_IUS_total] |
-6.91 | -9.67 – -4.16 | <0.001 |
| Random Effects | |||
| σ2 | 29.75 | ||
| Ï„00 ID | 59.99 | ||
| ICC | 0.67 | ||
| N ID | 259 | ||
| Observations | 488 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.679 | ||
parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.02 | [-0.30, 0.25]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.54]
## TimeD_M1_IUS_total | 0.23 | [-0.01, 0.47]
## GroupB_Controls:TimeD_M1_IUS_total | -0.41 | [-0.70, -0.13]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.72 | [-1.01, -0.43]plot_model(IUS_MEM_B1M, type = "int")
H2a: difference in change in behavioural IUS over time between groups (baseline to post)
Not excluding never samplers
# Excluding them at pre and post
BT_BP <- BT_PRE_POST %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")## Formatting table as needed
BT_BP_long <- BT_BP %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")BT_MEM_BP <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long, REML = TRUE)
summary(BT_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long
##
## REML criterion at convergence: 4395.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9264 -0.2398 -0.0766 0.0958 8.1574
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 354 18.81
## Residual 263 16.22
## Number of obs: 488, groups: ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 13.5745 3.6233 364.5272 3.746
## GroupB_Controls 3.6055 4.3930 364.5272 0.821
## GroupC_Intervention -1.9583 4.4001 364.5272 -0.445
## TimeB_POST_samples -0.5106 3.3457 240.3829 -0.153
## GroupB_Controls:TimeB_POST_samples -7.2867 4.0668 240.9058 -1.792
## GroupC_Intervention:TimeB_POST_samples -2.4873 4.0736 240.9148 -0.611
## Pr(>|t|)
## (Intercept) 0.000208 ***
## GroupB_Controls 0.412330
## GroupC_Intervention 0.656542
## TimeB_POST_samples 0.878821
## GroupB_Controls:TimeB_POST_samples 0.074429 .
## GroupC_Intervention:TimeB_POST_samples 0.542042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.825
## GrpC_Intrvn -0.823 0.679
## TmB_POST_sm -0.462 0.381 0.380
## GB_C:TB_POS 0.380 -0.461 -0.313 -0.823
## GC_I:TB_POS 0.379 -0.313 -0.460 -0.821 0.676anova (BT_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 322.16 161.08 2 243.67 0.6124 0.54290
## Time 1532.59 1532.59 1 241.18 5.8262 0.01653 *
## Group:Time 1018.76 509.38 2 241.35 1.9364 0.14645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(BT_MEM_BP)| Â | BT Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 13.57 | 6.45 – 20.69 | <0.001 |
| Group [B_Controls] | 3.61 | -5.03 – 12.24 | 0.412 |
| Group [C_Intervention] | -1.96 | -10.60 – 6.69 | 0.656 |
| Time [B_POST_samples] | -0.51 | -7.08 – 6.06 | 0.879 |
|
Group [B_Controls] × Time [B_POST_samples] |
-7.29 | -15.28 – 0.70 | 0.074 |
|
Group [C_Intervention] × Time [B_POST_samples] |
-2.49 | -10.49 – 5.52 | 0.542 |
| Random Effects | |||
| σ2 | 263.05 | ||
| Ï„00 ID | 353.98 | ||
| ICC | 0.57 | ||
| N ID | 246 | ||
| Observations | 488 | ||
| Marginal R2 / Conditional R2 | 0.015 / 0.580 | ||
parameters::standardise_parameters(BT_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.23, 0.35]
## GroupB_Controls | 0.14 | [-0.20, 0.49]
## GroupC_Intervention | -0.08 | [-0.43, 0.27]
## TimeB_POST_samples | -0.02 | [-0.28, 0.24]
## GroupB_Controls:TimeB_POST_samples | -0.29 | [-0.61, 0.03]
## GroupC_Intervention:TimeB_POST_samples | -0.10 | [-0.42, 0.22]plot_model(BT_MEM_BP, type = "int")
Excluding never samplers at PRE
# Excluding them at pre and post
BT_BP_removed <- BT_PRE_POST %>%
filter(A_PRE_samples != "0") %>% # only excluding from PRE
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")## Formatting table as needed
BT_BP_long_removed <- BT_BP_removed %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")BT_MEM_BP_removed <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_removed, REML = TRUE)
summary(BT_MEM_BP_removed)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long_removed
##
## REML criterion at convergence: 3169.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0812 -0.2761 -0.0683 0.1403 6.8856
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 429.4 20.72
## Residual 358.4 18.93
## Number of obs: 343, groups: ID, 173
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 16.7895 4.5533 261.0959 3.687
## GroupB_Controls 9.2408 5.7157 261.0959 1.617
## GroupC_Intervention -0.1228 5.6702 261.0959 -0.022
## TimeB_POST_samples -1.1053 4.3434 168.0021 -0.254
## GroupB_Controls:TimeB_POST_samples -11.2692 5.4762 168.6705 -2.058
## GroupC_Intervention:TimeB_POST_samples -3.4718 5.4196 168.3090 -0.641
## Pr(>|t|)
## (Intercept) 0.000276 ***
## GroupB_Controls 0.107143
## GroupC_Intervention 0.982737
## TimeB_POST_samples 0.799442
## GroupB_Controls:TimeB_POST_samples 0.041143 *
## GroupC_Intervention:TimeB_POST_samples 0.522658
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.797
## GrpC_Intrvn -0.803 0.640
## TmB_POST_sm -0.477 0.380 0.383
## GB_C:TB_POS 0.378 -0.475 -0.304 -0.793
## GC_I:TB_POS 0.382 -0.304 -0.476 -0.801 0.636anova (BT_MEM_BP_removed)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 601.59 300.79 2 170.47 0.8392 0.433833
## Time 2885.84 2885.84 1 168.72 8.0513 0.005105 **
## Group:Time 1785.79 892.90 2 168.82 2.4911 0.085856 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(BT_MEM_BP_removed)| Â | BT Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 16.79 | 7.83 – 25.75 | <0.001 |
| Group [B_Controls] | 9.24 | -2.00 – 20.48 | 0.107 |
| Group [C_Intervention] | -0.12 | -11.28 – 11.03 | 0.983 |
| Time [B_POST_samples] | -1.11 | -9.65 – 7.44 | 0.799 |
|
Group [B_Controls] × Time [B_POST_samples] |
-11.27 | -22.04 – -0.50 | 0.040 |
|
Group [C_Intervention] × Time [B_POST_samples] |
-3.47 | -14.13 – 7.19 | 0.522 |
| Random Effects | |||
| σ2 | 358.43 | ||
| Ï„00 ID | 429.41 | ||
| ICC | 0.55 | ||
| N ID | 173 | ||
| Observations | 343 | ||
| Marginal R2 / Conditional R2 | 0.028 / 0.558 | ||
parameters::standardise_parameters(BT_MEM_BP_removed)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -6.00e-03 | [-0.32, 0.31]
## GroupB_Controls | 0.33 | [-0.07, 0.72]
## GroupC_Intervention | -4.34e-03 | [-0.40, 0.39]
## TimeB_POST_samples | -0.04 | [-0.34, 0.26]
## GroupB_Controls:TimeB_POST_samples | -0.40 | [-0.78, -0.02]
## GroupC_Intervention:TimeB_POST_samples | -0.12 | [-0.50, 0.25]plot_model(BT_MEM_BP_removed, type = "int")
Excluding never samplers at both PRE and POST
# Excluding them at pre and post
BT_BP_removed_both <- BT_PRE_POST %>%
filter(A_PRE_samples != "0", B_POST_samples != "0") %>% # only excluding from PRE
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")## Formatting table as needed
BT_BP_long_removed_both <- BT_BP_removed_both %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")BT_MEM_BP_removed_both <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_removed_both, REML = TRUE)
summary(BT_MEM_BP_removed_both)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long_removed_both
##
## REML criterion at convergence: 2294.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8069 -0.2375 -0.0513 0.1451 5.7469
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 545.0 23.35
## Residual 206.9 14.38
## Number of obs: 256, groups: ID, 128
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 19.250 4.848 163.890 3.971
## GroupB_Controls 4.990 6.208 163.890 0.804
## GroupC_Intervention 3.685 6.312 163.890 0.584
## TimeB_POST_samples -0.625 3.596 125.000 -0.174
## GroupB_Controls:TimeB_POST_samples -5.895 4.605 125.000 -1.280
## GroupC_Intervention:TimeB_POST_samples -4.288 4.683 125.000 -0.916
## Pr(>|t|)
## (Intercept) 0.000107 ***
## GroupB_Controls 0.422661
## GroupC_Intervention 0.560191
## TimeB_POST_samples 0.862299
## GroupB_Controls:TimeB_POST_samples 0.202877
## GroupC_Intervention:TimeB_POST_samples 0.361562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.781
## GrpC_Intrvn -0.768 0.600
## TmB_POST_sm -0.371 0.290 0.285
## GB_C:TB_POS 0.290 -0.371 -0.222 -0.781
## GC_I:TB_POS 0.285 -0.222 -0.371 -0.768 0.600anova (BT_MEM_BP_removed_both)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 26.79 13.40 2 125 0.0647 0.93734
## Time 996.01 996.01 1 125 4.8141 0.03008 *
## Group:Time 346.12 173.06 2 125 0.8365 0.43565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(BT_MEM_BP_removed_both)| Â | BT Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 19.25 | 9.70 – 28.80 | <0.001 |
| Group [B_Controls] | 4.99 | -7.24 – 17.22 | 0.422 |
| Group [C_Intervention] | 3.68 | -8.75 – 16.12 | 0.560 |
| Time [B_POST_samples] | -0.62 | -7.71 – 6.46 | 0.862 |
|
Group [B_Controls] × Time [B_POST_samples] |
-5.90 | -14.97 – 3.18 | 0.202 |
|
Group [C_Intervention] × Time [B_POST_samples] |
-4.29 | -13.51 – 4.93 | 0.361 |
| Random Effects | |||
| σ2 | 206.89 | ||
| Ï„00 ID | 545.04 | ||
| ICC | 0.72 | ||
| N ID | 128 | ||
| Observations | 256 | ||
| Marginal R2 / Conditional R2 | 0.009 / 0.727 | ||
parameters::standardise_parameters(BT_MEM_BP_removed_both)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | -0.04 | [-0.39, 0.31]
## GroupB_Controls | 0.18 | [-0.27, 0.63]
## GroupC_Intervention | 0.14 | [-0.32, 0.59]
## TimeB_POST_samples | -0.02 | [-0.28, 0.24]
## GroupB_Controls:TimeB_POST_samples | -0.22 | [-0.55, 0.12]
## GroupC_Intervention:TimeB_POST_samples | -0.16 | [-0.50, 0.18]plot_model(BT_MEM_BP_removed_both, type = "int")
H2a: difference in change in growth mindsets over time between groups
GM_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM")## Formatting table as needed
GM_alltimepoints_long <- GM_alltimepoints %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM, C_W1_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_alltimepoints_long, REML = TRUE)
summary(GM_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_alltimepoints_long
##
## REML criterion at convergence: 3110.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1305 -0.5023 -0.0609 0.4554 3.6922
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.3098 1.1445
## Residual 0.7768 0.8814
## Number of obs: 996, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.74000 0.20429 463.07671 13.413
## GroupB_Controls 0.40151 0.24783 463.07671 1.620
## GroupC_Intervention 0.16291 0.24898 463.07671 0.654
## TimeB_POST_GM 0.04000 0.17627 728.13424 0.227
## TimeC_W1_GM -0.07589 0.17861 729.70994 -0.425
## TimeD_M1_GM -0.03987 0.18380 733.00453 -0.217
## GroupB_Controls:TimeB_POST_GM -0.53057 0.21384 728.13424 -2.481
## GroupC_Intervention:TimeB_POST_GM -0.71126 0.21528 728.38399 -3.304
## GroupB_Controls:TimeC_W1_GM -0.23713 0.21621 729.54890 -1.097
## GroupC_Intervention:TimeC_W1_GM -0.51437 0.21744 729.67920 -2.366
## GroupB_Controls:TimeD_M1_GM -0.35002 0.22306 733.13394 -1.569
## GroupC_Intervention:TimeD_M1_GM -0.58867 0.22393 733.05960 -2.629
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.10589
## GroupC_Intervention 0.51323
## TimeB_POST_GM 0.82055
## TimeC_W1_GM 0.67105
## TimeD_M1_GM 0.82834
## GroupB_Controls:TimeB_POST_GM 0.01332 *
## GroupC_Intervention:TimeB_POST_GM 0.00100 **
## GroupB_Controls:TimeC_W1_GM 0.27310
## GroupC_Intervention:TimeC_W1_GM 0.01826 *
## GroupB_Controls:TimeD_M1_GM 0.11704
## GroupC_Intervention:TimeD_M1_GM 0.00875 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS TC_W1_ TD_M1_ GB_C:TB GC_I:TB GB_C:TC
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_GM -0.431 0.356 0.354
## TimeC_W1_GM -0.426 0.351 0.349 0.493
## TimeD_M1_GM -0.414 0.341 0.340 0.480 0.472
## GB_C:TB_POS 0.356 -0.431 -0.292 -0.824 -0.407 -0.395
## GC_I:TB_POS 0.353 -0.291 -0.431 -0.819 -0.404 -0.393 0.675
## GB_C:TC_W1_ 0.352 -0.427 -0.289 -0.408 -0.826 -0.390 0.495 0.334
## GC_I:TC_W1_ 0.350 -0.288 -0.426 -0.405 -0.821 -0.388 0.334 0.493 0.679
## GB_C:TD_M1_ 0.341 -0.414 -0.280 -0.395 -0.389 -0.824 0.479 0.324 0.474
## GC_I:TD_M1_ 0.340 -0.280 -0.414 -0.394 -0.388 -0.821 0.324 0.479 0.320
## GC_I:TC GB_C:TD
## GrpB_Cntrls
## GrpC_Intrvn
## TmB_POST_GM
## TimeC_W1_GM
## TimeD_M1_GM
## GB_C:TB_POS
## GC_I:TB_POS
## GB_C:TC_W1_
## GC_I:TC_W1_
## GB_C:TD_M1_ 0.320
## GC_I:TD_M1_ 0.474 0.676anova (GM_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.6999 2.3499 2 256.22 3.0251 0.05029 .
## Time 21.1646 7.0549 3 731.30 9.0817 6.584e-06 ***
## Group:Time 10.6523 1.7754 6 731.29 2.2855 0.03414 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.34 – 3.14 | <0.001 |
| Group [B_Controls] | 0.40 | -0.08 – 0.89 | 0.106 |
| Group [C_Intervention] | 0.16 | -0.33 – 0.65 | 0.513 |
| Time [B_POST_GM] | 0.04 | -0.31 – 0.39 | 0.821 |
| Time [C_W1_GM] | -0.08 | -0.43 – 0.27 | 0.671 |
| Time [D_M1_GM] | -0.04 | -0.40 – 0.32 | 0.828 |
|
Group [B_Controls] × Time [B_POST_GM] |
-0.53 | -0.95 – -0.11 | 0.013 |
|
Group [C_Intervention] × Time [B_POST_GM] |
-0.71 | -1.13 – -0.29 | 0.001 |
|
Group [B_Controls] × Time [C_W1_GM] |
-0.24 | -0.66 – 0.19 | 0.273 |
|
Group [C_Intervention] × Time [C_W1_GM] |
-0.51 | -0.94 – -0.09 | 0.018 |
|
Group [B_Controls] × Time [D_M1_GM] |
-0.35 | -0.79 – 0.09 | 0.117 |
|
Group [C_Intervention] × Time [D_M1_GM] |
-0.59 | -1.03 – -0.15 | 0.009 |
| Random Effects | |||
| σ2 | 0.78 | ||
| Ï„00 ID | 1.31 | ||
| ICC | 0.63 | ||
| N ID | 259 | ||
| Observations | 996 | ||
| Marginal R2 / Conditional R2 | 0.037 / 0.641 | ||
parameters::standardise_parameters(GM_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 0.05 | [-0.22, 0.33]
## GroupB_Controls | 0.27 | [-0.06, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.44]
## TimeB_POST_GM | 0.03 | [-0.21, 0.26]
## TimeC_W1_GM | -0.05 | [-0.29, 0.19]
## TimeD_M1_GM | -0.03 | [-0.27, 0.22]
## GroupB_Controls:TimeB_POST_GM | -0.36 | [-0.65, -0.08]
## GroupC_Intervention:TimeB_POST_GM | -0.49 | [-0.77, -0.20]
## GroupB_Controls:TimeC_W1_GM | -0.16 | [-0.45, 0.13]
## GroupC_Intervention:TimeC_W1_GM | -0.35 | [-0.64, -0.06]
## GroupB_Controls:TimeD_M1_GM | -0.24 | [-0.54, 0.06]
## GroupC_Intervention:TimeD_M1_GM | -0.40 | [-0.70, -0.10]Effect of time in the intervention group
GM_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM") %>%
filter(Group == "C_Intervention")## Formatting table as needed
GM_I_long <- GM_I %>%
pivot_longer(cols = c("A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM"),
names_to = "Time",
values_to = "GM_Score")GM_MEM_I <- lmer(GM_Score ~ Time + (1|ID), data = GM_I_long, REML = TRUE)
summary(GM_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_I_long
##
## REML criterion at convergence: 1213.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6550 -0.4944 -0.0741 0.3975 3.7670
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.1927 1.0921
## Residual 0.7458 0.8636
## Number of obs: 395, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9029 0.1372 188.3310 21.160 < 2e-16 ***
## TimeB_POST_GM -0.6714 0.1211 289.4670 -5.544 6.65e-08 ***
## TimeC_W1_GM -0.5901 0.1215 289.7645 -4.857 1.96e-06 ***
## TimeD_M1_GM -0.6289 0.1253 291.2381 -5.018 9.11e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TB_POS TC_W1_
## TmB_POST_GM -0.436
## TimeC_W1_GM -0.434 0.492
## TimeD_M1_GM -0.421 0.477 0.477anova (GM_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 30.463 10.154 3 290.56 13.616 2.459e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_I)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.90 | 2.63 – 3.17 | <0.001 |
| Time [B_POST_GM] | -0.67 | -0.91 – -0.43 | <0.001 |
| Time [C_W1_GM] | -0.59 | -0.83 – -0.35 | <0.001 |
| Time [D_M1_GM] | -0.63 | -0.88 – -0.38 | <0.001 |
| Random Effects | |||
| σ2 | 0.75 | ||
| Ï„00 ID | 1.19 | ||
| ICC | 0.62 | ||
| N ID | 103 | ||
| Observations | 395 | ||
| Marginal R2 / Conditional R2 | 0.038 / 0.630 | ||
parameters::standardise_parameters(GM_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------
## (Intercept) | 0.33 | [ 0.14, 0.52]
## TimeB_POST_GM | -0.47 | [-0.64, -0.31]
## TimeC_W1_GM | -0.42 | [-0.59, -0.25]
## TimeD_M1_GM | -0.44 | [-0.62, -0.27]Baseline to post
GM_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM")## Formatting table as needed
GM_BP_long <- GM_BP %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_BP <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long, REML = TRUE)
summary(GM_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_BP_long
##
## REML criterion at convergence: 1673.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5473 -0.4356 -0.0320 0.4087 2.9611
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.471 1.2128
## Residual 0.614 0.7836
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2042 341.6189 13.418 < 2e-16
## GroupB_Controls 0.4015 0.2477 341.6189 1.621 0.105989
## GroupC_Intervention 0.1629 0.2489 341.6189 0.655 0.513178
## TimeB_POST_GM 0.0400 0.1567 254.5431 0.255 0.798745
## GroupB_Controls:TimeB_POST_GM -0.5306 0.1901 254.5431 -2.791 0.005657
## GroupC_Intervention:TimeB_POST_GM -0.7237 0.1915 254.9544 -3.779 0.000196
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeB_POST_GM
## GroupB_Controls:TimeB_POST_GM **
## GroupC_Intervention:TimeB_POST_GM ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_GM -0.384 0.316 0.315
## GB_C:TB_POS 0.316 -0.384 -0.260 -0.824
## GC_I:TB_POS 0.314 -0.259 -0.383 -0.818 0.674anova (GM_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.0352 1.0176 2 256.34 1.6574 0.1926771
## Time 16.3667 16.3667 1 254.86 26.6558 4.9e-07 ***
## Group:Time 8.8331 4.4165 2 254.92 7.1931 0.0009142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_BP)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.34 – 3.14 | <0.001 |
| Group [B_Controls] | 0.40 | -0.09 – 0.89 | 0.106 |
| Group [C_Intervention] | 0.16 | -0.33 – 0.65 | 0.513 |
| Time [B_POST_GM] | 0.04 | -0.27 – 0.35 | 0.799 |
|
Group [B_Controls] × Time [B_POST_GM] |
-0.53 | -0.90 – -0.16 | 0.005 |
|
Group [C_Intervention] × Time [B_POST_GM] |
-0.72 | -1.10 – -0.35 | <0.001 |
| Random Effects | |||
| σ2 | 0.61 | ||
| Ï„00 ID | 1.47 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.043 / 0.718 | ||
parameters::standardise_parameters(GM_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 1.11e-03 | [-0.27, 0.27]
## GroupB_Controls | 0.27 | [-0.06, 0.60]
## GroupC_Intervention | 0.11 | [-0.22, 0.44]
## TimeB_POST_GM | 0.03 | [-0.18, 0.24]
## GroupB_Controls:TimeB_POST_GM | -0.36 | [-0.61, -0.11]
## GroupC_Intervention:TimeB_POST_GM | -0.49 | [-0.75, -0.24]plot_model(GM_MEM_BP, type = "int")
Baseline to 1W
GM_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM")## Formatting table as needed
GM_B1W_long <- GM_B1W %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1W <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1W_long, REML = TRUE)
summary(GM_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1W_long
##
## REML criterion at convergence: 1747.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2664 -0.5109 -0.1518 0.4940 2.7870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.038 1.019
## Residual 1.010 1.005
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2024 403.7332 13.539 <2e-16
## GroupB_Controls 0.4015 0.2455 403.7332 1.635 0.1027
## GroupC_Intervention 0.1629 0.2467 403.7332 0.661 0.5093
## TimeC_W1_GM -0.0784 0.2041 253.9941 -0.384 0.7012
## GroupB_Controls:TimeC_W1_GM -0.2376 0.2470 253.3522 -0.962 0.3370
## GroupC_Intervention:TimeC_W1_GM -0.5133 0.2484 253.6544 -2.066 0.0398
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeC_W1_GM
## GroupB_Controls:TimeC_W1_GM
## GroupC_Intervention:TimeC_W1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeC_W1_GM -0.489 0.403 0.401
## GB_C:TC_W1_ 0.404 -0.490 -0.332 -0.826
## GC_I:TC_W1_ 0.402 -0.331 -0.490 -0.822 0.679anova (GM_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.0659 2.5329 2 256.72 2.5080 0.0834210 .
## Time 12.1159 12.1159 1 253.25 11.9969 0.0006256 ***
## Group:Time 4.6763 2.3381 2 253.08 2.3152 0.1008383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1W)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.34 – 3.14 | <0.001 |
| Group [B_Controls] | 0.40 | -0.08 – 0.88 | 0.103 |
| Group [C_Intervention] | 0.16 | -0.32 – 0.65 | 0.509 |
| Time [C_W1_GM] | -0.08 | -0.48 – 0.32 | 0.701 |
|
Group [B_Controls] × Time [C_W1_GM] |
-0.24 | -0.72 – 0.25 | 0.337 |
|
Group [C_Intervention] × Time [C_W1_GM] |
-0.51 | -1.00 – -0.03 | 0.039 |
| Random Effects | |||
| σ2 | 1.01 | ||
| Ï„00 ID | 1.04 | ||
| ICC | 0.51 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.524 | ||
parameters::standardise_parameters(GM_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.31, 0.24]
## GroupB_Controls | 0.28 | [-0.06, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeC_W1_GM | -0.05 | [-0.33, 0.22]
## GroupB_Controls:TimeC_W1_GM | -0.16 | [-0.50, 0.17]
## GroupC_Intervention:TimeC_W1_GM | -0.35 | [-0.69, -0.02]plot_model(GM_MEM_B1W, type = "int")
Baseline to 1M
GM_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM")## Formatting table as needed
GM_B1M_long <- GM_B1M %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1M <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1M_long, REML = TRUE)
summary(GM_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1M_long
##
## REML criterion at convergence: 1639.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3875 -0.5367 -0.1142 0.4999 2.8661
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.0920 1.0450
## Residual 0.8918 0.9444
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.19919 375.54541 13.756 <2e-16
## GroupB_Controls 0.40151 0.24164 375.54541 1.662 0.0974
## GroupC_Intervention 0.16291 0.24277 375.54541 0.671 0.5026
## TimeD_M1_GM -0.03867 0.19861 235.43449 -0.195 0.8458
## GroupB_Controls:TimeD_M1_GM -0.30728 0.24103 235.51016 -1.275 0.2036
## GroupC_Intervention:TimeD_M1_GM -0.59125 0.24193 235.33298 -2.444 0.0153
##
## (Intercept) ***
## GroupB_Controls .
## GroupC_Intervention
## TimeD_M1_GM
## GroupB_Controls:TimeD_M1_GM
## GroupC_Intervention:TimeD_M1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeD_M1_GM -0.451 0.372 0.370
## GB_C:TD_M1_ 0.372 -0.451 -0.305 -0.824
## GC_I:TD_M1_ 0.370 -0.305 -0.451 -0.821 0.676anova (GM_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3400 2.1700 2 254.05 2.4332 0.089803 .
## Time 11.8930 11.8930 1 235.41 13.3353 0.000321 ***
## Group:Time 5.5615 2.7807 2 235.41 3.1180 0.046079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1M)| Â | GM Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.35 – 3.13 | <0.001 |
| Group [B_Controls] | 0.40 | -0.07 – 0.88 | 0.097 |
| Group [C_Intervention] | 0.16 | -0.31 – 0.64 | 0.503 |
| Time [D_M1_GM] | -0.04 | -0.43 – 0.35 | 0.846 |
|
Group [B_Controls] × Time [D_M1_GM] |
-0.31 | -0.78 – 0.17 | 0.203 |
|
Group [C_Intervention] × Time [D_M1_GM] |
-0.59 | -1.07 – -0.12 | 0.015 |
| Random Effects | |||
| σ2 | 0.89 | ||
| Ï„00 ID | 1.09 | ||
| ICC | 0.55 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.039 / 0.568 | ||
parameters::standardise_parameters(GM_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.28 | [-0.05, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeD_M1_GM | -0.03 | [-0.30, 0.25]
## GroupB_Controls:TimeD_M1_GM | -0.21 | [-0.55, 0.12]
## GroupC_Intervention:TimeD_M1_GM | -0.41 | [-0.75, -0.08]plot_model(GM_MEM_B1M, type = "int")
H2b: difference in change in PHQ over time between groups
PHQ_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total")## Formatting table as needed
PHQ_alltimepoints_long <- PHQ_alltimepoints %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_alltimepoints_long, REML = TRUE)
summary(PHQ_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_alltimepoints_long
##
## REML criterion at convergence: 4431
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0848 -0.5178 -0.1105 0.4839 3.1316
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 23.09 4.805
## Residual 12.85 3.584
## Number of obs: 738, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.96000 0.84777 413.57028 11.748
## GroupB_Controls -0.51660 1.02847 413.57027 -0.502
## GroupC_Intervention 0.71961 1.03325 413.57027 0.696
## TimeC_W1_PHQ_total -0.02818 0.72719 476.89051 -0.039
## TimeD_M1_PHQ_total 0.65044 0.75641 482.04856 0.860
## GroupB_Controls:TimeC_W1_PHQ_total -0.89100 0.88115 476.94535 -1.011
## GroupC_Intervention:TimeC_W1_PHQ_total -1.43926 0.88515 476.71330 -1.626
## GroupB_Controls:TimeD_M1_PHQ_total -1.81031 0.91454 481.81570 -1.979
## GroupC_Intervention:TimeD_M1_PHQ_total -3.00912 0.91898 481.64066 -3.274
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.61572
## GroupC_Intervention 0.48654
## TimeC_W1_PHQ_total 0.96910
## TimeD_M1_PHQ_total 0.39027
## GroupB_Controls:TimeC_W1_PHQ_total 0.31244
## GroupC_Intervention:TimeC_W1_PHQ_total 0.10461
## GroupB_Controls:TimeD_M1_PHQ_total 0.04833 *
## GroupC_Intervention:TimeD_M1_PHQ_total 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ TD_M1_ GB_C:TC GC_I:TC GB_C:TD
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_PHQ_ -0.417 0.344 0.342
## TmD_M1_PHQ_ -0.401 0.330 0.329 0.465
## GB_C:TC_W1_ 0.344 -0.417 -0.282 -0.825 -0.384
## GC_I:TC_W1_ 0.342 -0.282 -0.417 -0.822 -0.382 0.678
## GB_C:TD_M1_ 0.331 -0.402 -0.272 -0.385 -0.827 0.469 0.316
## GC_I:TD_M1_ 0.330 -0.272 -0.402 -0.383 -0.823 0.316 0.468 0.681anova (PHQ_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 32.744 16.372 2 258.10 1.2743 0.28137
## Time 115.215 57.608 2 480.45 4.4840 0.01177 *
## Group:Time 139.414 34.854 4 480.31 2.7129 0.02949 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.96 | 8.30 – 11.62 | <0.001 |
| Group [B_Controls] | -0.52 | -2.54 – 1.50 | 0.616 |
| Group [C_Intervention] | 0.72 | -1.31 – 2.75 | 0.486 |
| Time [C_W1_PHQ_total] | -0.03 | -1.46 – 1.40 | 0.969 |
| Time [D_M1_PHQ_total] | 0.65 | -0.83 – 2.14 | 0.390 |
|
Group [B_Controls] × Time [C_W1_PHQ_total] |
-0.89 | -2.62 – 0.84 | 0.312 |
|
Group [C_Intervention] × Time [C_W1_PHQ_total] |
-1.44 | -3.18 – 0.30 | 0.104 |
|
Group [B_Controls] × Time [D_M1_PHQ_total] |
-1.81 | -3.61 – -0.01 | 0.048 |
|
Group [C_Intervention] × Time [D_M1_PHQ_total] |
-3.01 | -4.81 – -1.20 | 0.001 |
| Random Effects | |||
| σ2 | 12.85 | ||
| Ï„00 ID | 23.09 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 738 | ||
| Marginal R2 / Conditional R2 | 0.021 / 0.650 | ||
parameters::standardise_parameters(PHQ_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.11 | [-0.16, 0.39]
## GroupB_Controls | -0.09 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.45]
## TimeC_W1_PHQ_total | -4.66e-03 | [-0.24, 0.23]
## TimeD_M1_PHQ_total | 0.11 | [-0.14, 0.35]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.15 | [-0.43, 0.14]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.24 | [-0.53, 0.05]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.30 | [-0.60, 0.00]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.50 | [-0.80, -0.20]Effect of time in the intervention group
PHQ_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
PHQ_I_long <- PHQ_I %>%
pivot_longer(cols = c("A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total"),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_I <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_I_long, REML = TRUE)
summary(PHQ_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Time + (1 | ID)
## Data: PHQ_I_long
##
## REML criterion at convergence: 1764.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.98786 -0.51733 -0.09949 0.48087 2.91165
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.47 4.525
## Residual 13.32 3.650
## Number of obs: 294, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.6796 0.5728 173.5178 18.645 < 2e-16 ***
## TimeC_W1_PHQ_total -1.4672 0.5139 190.6547 -2.855 0.00478 **
## TimeD_M1_PHQ_total -2.3572 0.5312 192.6366 -4.437 1.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_PHQ_ -0.439
## TmD_M1_PHQ_ -0.425 0.473anova (PHQ_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 272.15 136.08 2 192.2 10.214 6.088e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_I)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 10.68 | 9.55 – 11.81 | <0.001 |
| Time [C_W1_PHQ_total] | -1.47 | -2.48 – -0.46 | 0.005 |
| Time [D_M1_PHQ_total] | -2.36 | -3.40 – -1.31 | <0.001 |
| Random Effects | |||
| σ2 | 13.32 | ||
| Ï„00 ID | 20.47 | ||
| ICC | 0.61 | ||
| N ID | 103 | ||
| Observations | 294 | ||
| Marginal R2 / Conditional R2 | 0.027 / 0.616 | ||
parameters::standardise_parameters(PHQ_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.21 | [ 0.01, 0.40]
## TimeC_W1_PHQ_total | -0.25 | [-0.42, -0.08]
## TimeD_M1_PHQ_total | -0.40 | [-0.58, -0.22]Effect of time in the Control group
PHQ_C <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
PHQ_C_long <- PHQ_C %>%
pivot_longer(cols = c("A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total"),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_C <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_C_long, REML = TRUE)
summary(PHQ_MEM_C)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Time + (1 | ID)
## Data: PHQ_C_long
##
## REML criterion at convergence: 1844.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6395 -0.5101 -0.1211 0.4373 2.9851
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 23.96 4.895
## Residual 14.15 3.762
## Number of obs: 303, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.4434 0.5996 172.5470 15.748 <2e-16 ***
## TimeC_W1_PHQ_total -0.9180 0.5223 196.3922 -1.758 0.0804 .
## TimeD_M1_PHQ_total -1.1616 0.5395 198.2254 -2.153 0.0325 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_PHQ_ -0.426
## TmD_M1_PHQ_ -0.413 0.476anova (PHQ_MEM_C)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 75.444 37.722 2 197.59 2.6649 0.0721 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_C)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.44 | 8.26 – 10.62 | <0.001 |
| Time [C_W1_PHQ_total] | -0.92 | -1.95 – 0.11 | 0.080 |
| Time [D_M1_PHQ_total] | -1.16 | -2.22 – -0.10 | 0.032 |
| Random Effects | |||
| σ2 | 14.15 | ||
| Ï„00 ID | 23.96 | ||
| ICC | 0.63 | ||
| N ID | 106 | ||
| Observations | 303 | ||
| Marginal R2 / Conditional R2 | 0.007 / 0.631 | ||
parameters::standardise_parameters(PHQ_MEM_C)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.11 | [-0.08, 0.30]
## TimeC_W1_PHQ_total | -0.15 | [-0.31, 0.02]
## TimeD_M1_PHQ_total | -0.19 | [-0.36, -0.02]Baseline to 1W
PHQ_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total")## Formatting table as needed
PHQ_B1W_long <- PHQ_B1W %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1W <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1W_long, REML = TRUE)
summary(PHQ_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1W_long
##
## REML criterion at convergence: 3069.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.70427 -0.49822 -0.04073 0.44204 3.13898
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 24.53 4.953
## Residual 10.07 3.173
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8318 339.2793 11.974
## GroupB_Controls -0.5166 1.0091 339.2793 -0.512
## GroupC_Intervention 0.7196 1.0138 339.2793 0.710
## TimeC_W1_PHQ_total -0.1012 0.6458 252.0371 -0.157
## GroupB_Controls:TimeC_W1_PHQ_total -0.7976 0.7821 251.8019 -1.020
## GroupC_Intervention:TimeC_W1_PHQ_total -1.3587 0.7858 251.8141 -1.729
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.609
## GroupC_Intervention 0.478
## TimeC_W1_PHQ_total 0.876
## GroupB_Controls:TimeC_W1_PHQ_total 0.309
## GroupC_Intervention:TimeC_W1_PHQ_total 0.085 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_PHQ_ -0.375 0.309 0.308
## GB_C:TC_W1_ 0.309 -0.375 -0.254 -0.826
## GC_I:TC_W1_ 0.308 -0.254 -0.375 -0.822 0.679anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 18.876 9.438 2 257.38 0.9375 0.39294
## Time 75.005 75.005 1 251.69 7.4503 0.00679 **
## Group:Time 30.533 15.266 2 251.61 1.5164 0.22149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1W)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.96 | 8.33 – 11.59 | <0.001 |
| Group [B_Controls] | -0.52 | -2.50 – 1.47 | 0.609 |
| Group [C_Intervention] | 0.72 | -1.27 – 2.71 | 0.478 |
| Time [C_W1_PHQ_total] | -0.10 | -1.37 – 1.17 | 0.875 |
|
Group [B_Controls] × Time [C_W1_PHQ_total] |
-0.80 | -2.33 – 0.74 | 0.308 |
|
Group [C_Intervention] × Time [C_W1_PHQ_total] |
-1.36 | -2.90 – 0.19 | 0.084 |
| Random Effects | |||
| σ2 | 10.07 | ||
| Ï„00 ID | 24.53 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.015 / 0.713 | ||
parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.07 | [-0.21, 0.34]
## GroupB_Controls | -0.09 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.46]
## TimeC_W1_PHQ_total | -0.02 | [-0.23, 0.20]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.13 | [-0.39, 0.13]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.23 | [-0.49, 0.03]plot_model(PHQ_MEM_B1W, type = "int")
Baseline to 1M
PHQ_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total")## Formatting table as needed
PHQ_B1M_long <- PHQ_B1M %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1M <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long, REML = TRUE)
summary(PHQ_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1M_long
##
## REML criterion at convergence: 3028.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64448 -0.55044 -0.09744 0.49965 2.79846
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.46 4.633
## Residual 15.15 3.892
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8557 367.8141 11.640
## GroupB_Controls -0.5166 1.0381 367.8141 -0.498
## GroupC_Intervention 0.7196 1.0429 367.8141 0.690
## TimeD_M1_PHQ_total 0.6244 0.8271 240.0648 0.755
## GroupB_Controls:TimeD_M1_PHQ_total -1.8086 0.9995 239.3565 -1.810
## GroupC_Intervention:TimeD_M1_PHQ_total -2.9746 1.0045 239.4295 -2.961
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.61902
## GroupC_Intervention 0.49062
## TimeD_M1_PHQ_total 0.45104
## GroupB_Controls:TimeD_M1_PHQ_total 0.07161 .
## GroupC_Intervention:TimeD_M1_PHQ_total 0.00337 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_PHQ_ -0.428 0.353 0.351
## GB_C:TD_M1_ 0.354 -0.430 -0.291 -0.828
## GC_I:TD_M1_ 0.352 -0.291 -0.430 -0.823 0.681anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 35.957 17.979 2 258.66 1.1869 0.30684
## Time 96.897 96.897 1 239.05 6.3966 0.01208 *
## Group:Time 134.059 67.029 2 238.81 4.4250 0.01297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1M)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.96 | 8.28 – 11.64 | <0.001 |
| Group [B_Controls] | -0.52 | -2.56 – 1.52 | 0.619 |
| Group [C_Intervention] | 0.72 | -1.33 – 2.77 | 0.491 |
| Time [D_M1_PHQ_total] | 0.62 | -1.00 – 2.25 | 0.451 |
|
Group [B_Controls] × Time [D_M1_PHQ_total] |
-1.81 | -3.77 – 0.16 | 0.071 |
|
Group [C_Intervention] × Time [D_M1_PHQ_total] |
-2.97 | -4.95 – -1.00 | 0.003 |
| Random Effects | |||
| σ2 | 15.15 | ||
| Ï„00 ID | 21.46 | ||
| ICC | 0.59 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.026 / 0.597 | ||
parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.37]
## GroupB_Controls | -0.08 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.45]
## TimeD_M1_PHQ_total | 0.10 | [-0.16, 0.37]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.30 | [-0.62, 0.03]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.49 | [-0.81, -0.16]plot_model(PHQ_MEM_B1M, type = "int")
H2b: difference in change in GAD over time between groups
# Merging across timepoints
GAD_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total")## Formatting table as needed
GAD_alltimepoints_long <- GAD_alltimepoints %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_alltimepoints_long, REML = TRUE)
summary(GAD_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_alltimepoints_long
##
## REML criterion at convergence: 4328.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8624 -0.4778 -0.0791 0.5074 3.3297
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.33 4.619
## Residual 11.00 3.316
## Number of obs: 737, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.8041 404.0648 9.974
## GroupB_Controls 0.4706 0.9754 404.0648 0.482
## GroupC_Intervention 1.2616 0.9800 404.0648 1.287
## TimeC_W1_GAD_total 0.4486 0.6728 475.9352 0.667
## TimeD_M1_GAD_total 1.3098 0.6999 480.7965 1.871
## GroupB_Controls:TimeC_W1_GAD_total -1.0910 0.8152 475.9947 -1.338
## GroupC_Intervention:TimeC_W1_GAD_total -1.4673 0.8189 475.7681 -1.792
## GroupB_Controls:TimeD_M1_GAD_total -2.2734 0.8473 480.7294 -2.683
## GroupC_Intervention:TimeD_M1_GAD_total -3.1116 0.8503 480.4121 -3.659
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.629772
## GroupC_Intervention 0.198718
## TimeC_W1_GAD_total 0.505237
## TimeD_M1_GAD_total 0.061903 .
## GroupB_Controls:TimeC_W1_GAD_total 0.181430
## GroupC_Intervention:TimeC_W1_GAD_total 0.073803 .
## GroupB_Controls:TimeD_M1_GAD_total 0.007544 **
## GroupC_Intervention:TimeD_M1_GAD_total 0.000281 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ TD_M1_ GB_C:TC GC_I:TC GB_C:TD
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_GAD_ -0.407 0.335 0.334
## TmD_M1_GAD_ -0.391 0.322 0.321 0.465
## GB_C:TC_W1_ 0.335 -0.407 -0.275 -0.825 -0.384
## GC_I:TC_W1_ 0.334 -0.275 -0.407 -0.822 -0.382 0.678
## GB_C:TD_M1_ 0.323 -0.392 -0.265 -0.384 -0.826 0.468 0.316
## GC_I:TD_M1_ 0.322 -0.265 -0.392 -0.383 -0.823 0.316 0.468 0.680anova (GAD_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.101 3.551 2 258.25 0.3229 0.724313
## Time 29.410 14.705 2 479.37 1.3374 0.263498
## Group:Time 148.130 37.033 4 479.25 3.3681 0.009839 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.02 | 6.44 – 9.60 | <0.001 |
| Group [B_Controls] | 0.47 | -1.44 – 2.39 | 0.630 |
| Group [C_Intervention] | 1.26 | -0.66 – 3.19 | 0.198 |
| Time [C_W1_GAD_total] | 0.45 | -0.87 – 1.77 | 0.505 |
| Time [D_M1_GAD_total] | 1.31 | -0.06 – 2.68 | 0.062 |
|
Group [B_Controls] × Time [C_W1_GAD_total] |
-1.09 | -2.69 – 0.51 | 0.181 |
|
Group [C_Intervention] × Time [C_W1_GAD_total] |
-1.47 | -3.07 – 0.14 | 0.074 |
|
Group [B_Controls] × Time [D_M1_GAD_total] |
-2.27 | -3.94 – -0.61 | 0.007 |
|
Group [C_Intervention] × Time [D_M1_GAD_total] |
-3.11 | -4.78 – -1.44 | <0.001 |
| Random Effects | |||
| σ2 | 11.00 | ||
| Ï„00 ID | 21.33 | ||
| ICC | 0.66 | ||
| N ID | 259 | ||
| Observations | 737 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.664 | ||
parameters::standardise_parameters(GAD_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.04 | [-0.31, 0.24]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.12, 0.56]
## TimeC_W1_GAD_total | 0.08 | [-0.15, 0.31]
## TimeD_M1_GAD_total | 0.23 | [-0.01, 0.47]
## GroupB_Controls:TimeC_W1_GAD_total | -0.19 | [-0.47, 0.09]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.26 | [-0.54, 0.02]
## GroupB_Controls:TimeD_M1_GAD_total | -0.40 | [-0.69, -0.11]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.54 | [-0.84, -0.25]Effect of time in the intervention group
GAD_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
GAD_I_long <- GAD_I %>%
pivot_longer(cols = c("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_I <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_I_long, REML = TRUE)
summary(GAD_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_I_long
##
## REML criterion at convergence: 1726.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8905 -0.4758 -0.0769 0.5231 2.7194
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.53 4.640
## Residual 10.86 3.296
## Number of obs: 294, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.2816 0.5608 160.4682 16.552 < 2e-16 ***
## TimeC_W1_GAD_total -1.0187 0.4640 190.5491 -2.195 0.029351 *
## TimeD_M1_GAD_total -1.8024 0.4800 192.1943 -3.755 0.000229 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_GAD_ -0.405
## TmD_M1_GAD_ -0.392 0.472anova (GAD_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 155.64 77.819 2 191.83 7.1649 0.0009978 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_I)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.28 | 8.18 – 10.39 | <0.001 |
| Time [C_W1_GAD_total] | -1.02 | -1.93 – -0.11 | 0.029 |
| Time [D_M1_GAD_total] | -1.80 | -2.75 – -0.86 | <0.001 |
| Random Effects | |||
| σ2 | 10.86 | ||
| Ï„00 ID | 21.53 | ||
| ICC | 0.66 | ||
| N ID | 103 | ||
| Observations | 294 | ||
| Marginal R2 / Conditional R2 | 0.016 / 0.670 | ||
parameters::standardise_parameters(GAD_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.15 | [-0.04, 0.34]
## TimeC_W1_GAD_total | -0.18 | [-0.34, -0.02]
## TimeD_M1_GAD_total | -0.31 | [-0.48, -0.15]Effect of time in the Control group
GAD_C <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
GAD_C_long <- GAD_C %>%
pivot_longer(cols = c("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_C <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_C_long, REML = TRUE)
summary(GAD_MEM_C)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_C_long
##
## REML criterion at convergence: 1788.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7651 -0.4670 -0.1016 0.5054 3.2095
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.48 4.741
## Residual 11.47 3.387
## Number of obs: 302, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.4906 0.5659 164.7207 15.003 <2e-16 ***
## TimeC_W1_GAD_total -0.6425 0.4703 195.2577 -1.366 0.1734
## TimeD_M1_GAD_total -0.9632 0.4877 197.0825 -1.975 0.0497 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_GAD_ -0.407
## TmD_M1_GAD_ -0.392 0.474anova (GAD_MEM_C)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 47.478 23.739 2 196.44 2.0693 0.129sjPlot::tab_model(GAD_MEM_C)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.49 | 7.38 – 9.60 | <0.001 |
| Time [C_W1_GAD_total] | -0.64 | -1.57 – 0.28 | 0.173 |
| Time [D_M1_GAD_total] | -0.96 | -1.92 – -0.00 | 0.049 |
| Random Effects | |||
| σ2 | 11.47 | ||
| Ï„00 ID | 22.48 | ||
| ICC | 0.66 | ||
| N ID | 106 | ||
| Observations | 302 | ||
| Marginal R2 / Conditional R2 | 0.005 / 0.664 | ||
parameters::standardise_parameters(GAD_MEM_C)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.09 | [-0.10, 0.29]
## TimeC_W1_GAD_total | -0.11 | [-0.27, 0.05]
## TimeD_M1_GAD_total | -0.17 | [-0.33, 0.00]Baseline to 1W
# Merging across timepoints
GAD_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total")## Formatting table as needed
GAD_B1W_long <- GAD_B1W %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1W <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1W_long, REML = TRUE)
summary(GAD_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1W_long
##
## REML criterion at convergence: 3025.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0453 -0.4584 -0.0843 0.4595 3.1840
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.202 4.712
## Residual 9.297 3.049
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.7937 340.7093 10.104
## GroupB_Controls 0.4706 0.9629 340.7093 0.489
## GroupC_Intervention 1.2616 0.9674 340.7093 1.304
## TimeC_W1_GAD_total 0.3104 0.6206 252.2282 0.500
## GroupB_Controls:TimeC_W1_GAD_total -0.9286 0.7516 251.9901 -1.236
## GroupC_Intervention:TimeC_W1_GAD_total -1.3167 0.7551 252.0024 -1.744
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6254
## GroupC_Intervention 0.1931
## TimeC_W1_GAD_total 0.6174
## GroupB_Controls:TimeC_W1_GAD_total 0.2178
## GroupC_Intervention:TimeC_W1_GAD_total 0.0824 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_GAD_ -0.378 0.311 0.310
## GB_C:TC_W1_ 0.312 -0.378 -0.256 -0.826
## GC_I:TC_W1_ 0.310 -0.256 -0.378 -0.822 0.679anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.671 3.8355 2 257.54 0.4125 0.6624
## Time 21.411 21.4112 1 251.88 2.3030 0.1304
## Group:Time 28.314 14.1572 2 251.80 1.5228 0.2201sjPlot::tab_model(GAD_MEM_B1W)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.02 | 6.46 – 9.58 | <0.001 |
| Group [B_Controls] | 0.47 | -1.42 – 2.36 | 0.625 |
| Group [C_Intervention] | 1.26 | -0.64 – 3.16 | 0.193 |
| Time [C_W1_GAD_total] | 0.31 | -0.91 – 1.53 | 0.617 |
|
Group [B_Controls] × Time [C_W1_GAD_total] |
-0.93 | -2.41 – 0.55 | 0.217 |
|
Group [C_Intervention] × Time [C_W1_GAD_total] |
-1.32 | -2.80 – 0.17 | 0.082 |
| Random Effects | |||
| σ2 | 9.30 | ||
| Ï„00 ID | 22.20 | ||
| ICC | 0.70 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.007 / 0.707 | ||
parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | -0.07 | [-0.35, 0.20]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.11, 0.56]
## TimeC_W1_GAD_total | 0.06 | [-0.16, 0.27]
## GroupB_Controls:TimeC_W1_GAD_total | -0.17 | [-0.43, 0.10]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.23 | [-0.50, 0.03]plot_model(GAD_MEM_B1W, type = "int")
Baseline to 1M
# Merging across timepoints
GAD_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total")## Formatting table as needed
GAD_B1M_long <- GAD_B1M %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1M <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1M_long, REML = TRUE)
summary(GAD_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1M_long
##
## REML criterion at convergence: 2951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26184 -0.51370 -0.07266 0.47118 2.72886
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.51 4.417
## Residual 12.64 3.555
## Number of obs: 486, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.8018 361.1090 10.002
## GroupB_Controls 0.4706 0.9727 361.1090 0.484
## GroupC_Intervention 1.2616 0.9772 361.1090 1.291
## TimeD_M1_GAD_total 1.2587 0.7560 238.1128 1.665
## GroupB_Controls:TimeD_M1_GAD_total -2.2184 0.9147 237.6716 -2.425
## GroupC_Intervention:TimeD_M1_GAD_total -3.0251 0.9181 237.5072 -3.295
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.62884
## GroupC_Intervention 0.19755
## TimeD_M1_GAD_total 0.09724 .
## GroupB_Controls:TimeD_M1_GAD_total 0.01605 *
## GroupC_Intervention:TimeD_M1_GAD_total 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_GAD_ -0.417 0.344 0.342
## GB_C:TD_M1_ 0.345 -0.418 -0.283 -0.826
## GC_I:TD_M1_ 0.343 -0.283 -0.418 -0.823 0.681anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.574 3.787 2 258.32 0.2997 0.741299
## Time 24.554 24.554 1 237.32 1.9431 0.164633
## Group:Time 137.971 68.986 2 237.14 5.4594 0.004808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_B1M)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.02 | 6.44 – 9.60 | <0.001 |
| Group [B_Controls] | 0.47 | -1.44 – 2.38 | 0.629 |
| Group [C_Intervention] | 1.26 | -0.66 – 3.18 | 0.197 |
| Time [D_M1_GAD_total] | 1.26 | -0.23 – 2.74 | 0.097 |
|
Group [B_Controls] × Time [D_M1_GAD_total] |
-2.22 | -4.02 – -0.42 | 0.016 |
|
Group [C_Intervention] × Time [D_M1_GAD_total] |
-3.03 | -4.83 – -1.22 | 0.001 |
| Random Effects | |||
| σ2 | 12.64 | ||
| Ï„00 ID | 19.51 | ||
| ICC | 0.61 | ||
| N ID | 259 | ||
| Observations | 486 | ||
| Marginal R2 / Conditional R2 | 0.016 / 0.613 | ||
parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.05 | [-0.32, 0.23]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.12, 0.56]
## TimeD_M1_GAD_total | 0.22 | [-0.04, 0.48]
## GroupB_Controls:TimeD_M1_GAD_total | -0.39 | [-0.70, -0.07]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.53 | [-0.85, -0.21]plot_model(GAD_MEM_B1M, type = "int")
H2b: difference in change in mood over time between groups
Mood_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "B_POST_mood_mean", "C_W1_mood_mean", "D_M1_mood_mean")## Formatting tables as needed
Mood_alltimepoints_long <- Mood_alltimepoints %>%
pivot_longer(cols = c("A_PRE_mood_mean", "B_POST_mood_mean", "C_W1_mood_mean", "D_M1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_alltimepoints_long, REML = TRUE)
summary(Mood_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_alltimepoints_long
##
## REML criterion at convergence: 9998.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9245 -0.4840 0.0543 0.5791 3.4075
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 892.9 29.88
## Residual 982.7 31.35
## Number of obs: 995, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.300 6.125 599.204 6.580
## GroupB_Controls -2.984 7.430 599.204 -0.402
## GroupC_Intervention -10.669 7.465 599.204 -1.429
## TimeB_POST_mood_mean -0.020 6.270 729.243 -0.003
## TimeC_W1_mood_mean -14.945 6.350 731.731 -2.353
## TimeD_M1_mood_mean -21.095 6.579 738.302 -3.206
## GroupB_Controls:TimeB_POST_mood_mean 19.992 7.606 729.243 2.628
## GroupC_Intervention:TimeB_POST_mood_mean 29.483 7.649 729.438 3.855
## GroupB_Controls:TimeC_W1_mood_mean 8.695 7.695 731.631 1.130
## GroupC_Intervention:TimeC_W1_mood_mean 9.945 7.739 731.707 1.285
## GroupB_Controls:TimeD_M1_mood_mean 14.488 7.957 737.849 1.821
## GroupC_Intervention:TimeD_M1_mood_mean 14.299 7.996 737.788 1.788
## Pr(>|t|)
## (Intercept) 1.03e-10 ***
## GroupB_Controls 0.688120
## GroupC_Intervention 0.153456
## TimeB_POST_mood_mean 0.997456
## TimeC_W1_mood_mean 0.018866 *
## TimeD_M1_mood_mean 0.001403 **
## GroupB_Controls:TimeB_POST_mood_mean 0.008758 **
## GroupC_Intervention:TimeB_POST_mood_mean 0.000126 ***
## GroupB_Controls:TimeC_W1_mood_mean 0.258872
## GroupC_Intervention:TimeC_W1_mood_mean 0.199181
## GroupB_Controls:TimeD_M1_mood_mean 0.069040 .
## GroupC_Intervention:TimeD_M1_mood_mean 0.074150 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS TC_W1_ TD_M1_ GB_C:TB GC_I:TB GB_C:TC
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_m_ -0.512 0.422 0.420
## TmC_W1_md_m -0.505 0.417 0.415 0.494
## TmD_M1_md_m -0.488 0.402 0.400 0.476 0.469
## GB_C:TB_POS 0.422 -0.512 -0.346 -0.824 -0.407 -0.393
## GC_I:TB_POS 0.420 -0.346 -0.511 -0.820 -0.405 -0.391 0.676
## GB_C:TC_W1_ 0.417 -0.506 -0.342 -0.407 -0.825 -0.387 0.494 0.334
## GC_I:TC_W1_ 0.415 -0.342 -0.505 -0.405 -0.821 -0.385 0.334 0.493 0.677
## GB_C:TD_M1_ 0.403 -0.489 -0.331 -0.394 -0.388 -0.827 0.478 0.323 0.472
## GC_I:TD_M1_ 0.401 -0.331 -0.489 -0.392 -0.386 -0.823 0.323 0.477 0.319
## GC_I:TC GB_C:TD
## GrpB_Cntrls
## GrpC_Intrvn
## TmB_POST_m_
## TmC_W1_md_m
## TmD_M1_md_m
## GB_C:TB_POS
## GC_I:TB_POS
## GB_C:TC_W1_
## GC_I:TC_W1_
## GB_C:TD_M1_ 0.319
## GC_I:TD_M1_ 0.471 0.680anova (Mood_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2110 1055 2 258.46 1.0734 0.34336
## Time 104490 34830 3 734.51 35.4434 < 2e-16 ***
## Group:Time 15983 2664 6 734.42 2.7107 0.01307 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 40.30 | 28.28 – 52.32 | <0.001 |
| Group [B_Controls] | -2.98 | -17.56 – 11.60 | 0.688 |
| Group [C_Intervention] | -10.67 | -25.32 – 3.98 | 0.153 |
| Time [B_POST_mood_mean] | -0.02 | -12.32 – 12.28 | 0.997 |
| Time [C_W1_mood_mean] | -14.94 | -27.41 – -2.48 | 0.019 |
| Time [D_M1_mood_mean] | -21.09 | -34.01 – -8.18 | 0.001 |
|
Group [B_Controls] × Time [B_POST_mood_mean] |
19.99 | 5.07 – 34.92 | 0.009 |
|
Group [C_Intervention] × Time [B_POST_mood_mean] |
29.48 | 14.47 – 44.49 | <0.001 |
|
Group [B_Controls] × Time [C_W1_mood_mean] |
8.69 | -6.41 – 23.80 | 0.259 |
|
Group [C_Intervention] × Time [C_W1_mood_mean] |
9.94 | -5.24 – 25.13 | 0.199 |
|
Group [B_Controls] × Time [D_M1_mood_mean] |
14.49 | -1.13 – 30.10 | 0.069 |
|
Group [C_Intervention] × Time [D_M1_mood_mean] |
14.30 | -1.39 – 29.99 | 0.074 |
| Random Effects | |||
| σ2 | 982.69 | ||
| Ï„00 ID | 892.93 | ||
| ICC | 0.48 | ||
| N ID | 259 | ||
| Observations | 995 | ||
| Marginal R2 / Conditional R2 | 0.079 / 0.517 | ||
parameters::standardise_parameters(Mood_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.36]
## GroupB_Controls | -0.07 | [-0.39, 0.26]
## GroupC_Intervention | -0.24 | [-0.56, 0.09]
## TimeB_POST_mood_mean | -4.45e-04 | [-0.27, 0.27]
## TimeC_W1_mood_mean | -0.33 | [-0.61, -0.06]
## TimeD_M1_mood_mean | -0.47 | [-0.76, -0.18]
## GroupB_Controls:TimeB_POST_mood_mean | 0.44 | [ 0.11, 0.78]
## GroupC_Intervention:TimeB_POST_mood_mean | 0.66 | [ 0.32, 0.99]
## GroupB_Controls:TimeC_W1_mood_mean | 0.19 | [-0.14, 0.53]
## GroupC_Intervention:TimeC_W1_mood_mean | 0.22 | [-0.12, 0.56]
## GroupB_Controls:TimeD_M1_mood_mean | 0.32 | [-0.03, 0.67]
## GroupC_Intervention:TimeD_M1_mood_mean | 0.32 | [-0.03, 0.67]Baseline to post
Mood_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "B_POST_mood_mean")## Formatting tables as needed
Mood_BP_long <- Mood_BP %>%
pivot_longer(cols = c("A_PRE_mood_mean", "B_POST_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_BP <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_BP_long, REML = TRUE)
summary(Mood_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_BP_long
##
## REML criterion at convergence: 5079
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1547 -0.3855 0.0476 0.4451 3.3781
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1035.6 32.18
## Residual 513.4 22.66
## Number of obs: 517, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.300 5.566 353.535 7.240
## GroupB_Controls -2.984 6.752 353.535 -0.442
## GroupC_Intervention -10.669 6.784 353.535 -1.573
## TimeB_POST_mood_mean -0.020 4.532 255.115 -0.004
## GroupB_Controls:TimeB_POST_mood_mean 19.992 5.498 255.115 3.637
## GroupC_Intervention:TimeB_POST_mood_mean 29.341 5.530 255.341 5.305
## Pr(>|t|)
## (Intercept) 2.81e-12 ***
## GroupB_Controls 0.658816
## GroupC_Intervention 0.116674
## TimeB_POST_mood_mean 0.996482
## GroupB_Controls:TimeB_POST_mood_mean 0.000334 ***
## GroupC_Intervention:TimeB_POST_mood_mean 2.44e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_m_ -0.407 0.336 0.334
## GB_C:TB_POS 0.336 -0.407 -0.275 -0.824
## GC_I:TB_POS 0.334 -0.275 -0.407 -0.819 0.675anova (Mood_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 677.9 339.0 2 255.99 0.6603 0.5176
## Time 30949.7 30949.7 1 255.29 60.2843 1.989e-13 ***
## Group:Time 14453.2 7226.6 2 255.32 14.0761 1.588e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_BP)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 40.30 | 29.36 – 51.24 | <0.001 |
| Group [B_Controls] | -2.98 | -16.25 – 10.28 | 0.659 |
| Group [C_Intervention] | -10.67 | -24.00 – 2.66 | 0.116 |
| Time [B_POST_mood_mean] | -0.02 | -8.92 – 8.88 | 0.996 |
|
Group [B_Controls] × Time [B_POST_mood_mean] |
19.99 | 9.19 – 30.79 | <0.001 |
|
Group [C_Intervention] × Time [B_POST_mood_mean] |
29.34 | 18.48 – 40.21 | <0.001 |
| Random Effects | |||
| σ2 | 513.40 | ||
| Ï„00 ID | 1035.58 | ||
| ICC | 0.67 | ||
| N ID | 259 | ||
| Observations | 517 | ||
| Marginal R2 / Conditional R2 | 0.079 / 0.695 | ||
parameters::standardise_parameters(Mood_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------------
## (Intercept) | -0.11 | [-0.38, 0.16]
## GroupB_Controls | -0.07 | [-0.40, 0.25]
## GroupC_Intervention | -0.26 | [-0.59, 0.07]
## TimeB_POST_mood_mean | -4.90e-04 | [-0.22, 0.22]
## GroupB_Controls:TimeB_POST_mood_mean | 0.49 | [ 0.23, 0.75]
## GroupC_Intervention:TimeB_POST_mood_mean | 0.72 | [ 0.45, 0.98]plot_model(Mood_MEM_BP, type = "int")
Baseline to 1W
Mood_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "C_W1_mood_mean")## Formatting tables as needed
Mood_B1W_long <- Mood_B1W %>%
pivot_longer(cols = c("A_PRE_mood_mean", "C_W1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_B1W <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_B1W_long, REML = TRUE)
summary(Mood_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_B1W_long
##
## REML criterion at convergence: 5220.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1570 -0.4429 0.1159 0.5899 2.4716
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 866.2 29.43
## Residual 1117.7 33.43
## Number of obs: 509, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.300 6.299 424.920 6.398
## GroupB_Controls -2.984 7.641 424.920 -0.390
## GroupC_Intervention -10.669 7.677 424.920 -1.390
## TimeC_W1_mood_mean -14.705 6.786 253.748 -2.167
## GroupB_Controls:TimeC_W1_mood_mean 8.265 8.220 253.359 1.005
## GroupC_Intervention:TimeC_W1_mood_mean 9.377 8.269 253.708 1.134
## Pr(>|t|)
## (Intercept) 4.16e-10 ***
## GroupB_Controls 0.6964
## GroupC_Intervention 0.1653
## TimeC_W1_mood_mean 0.0312 *
## GroupB_Controls:TimeC_W1_mood_mean 0.3157
## GroupC_Intervention:TimeC_W1_mood_mean 0.2579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_md_m -0.523 0.431 0.429
## GB_C:TC_W1_ 0.432 -0.524 -0.354 -0.825
## GC_I:TC_W1_ 0.429 -0.354 -0.523 -0.821 0.677anova (Mood_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2236.2 1118.1 2 257.40 1.0004 0.36916
## Time 8711.4 8711.4 1 253.43 7.7944 0.00564 **
## Group:Time 1553.5 776.7 2 253.35 0.6950 0.50003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_B1W)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 40.30 | 27.92 – 52.68 | <0.001 |
| Group [B_Controls] | -2.98 | -18.00 – 12.03 | 0.696 |
| Group [C_Intervention] | -10.67 | -25.75 – 4.41 | 0.165 |
| Time [C_W1_mood_mean] | -14.70 | -28.04 – -1.37 | 0.031 |
|
Group [B_Controls] × Time [C_W1_mood_mean] |
8.26 | -7.89 – 24.42 | 0.315 |
|
Group [C_Intervention] × Time [C_W1_mood_mean] |
9.38 | -6.87 – 25.62 | 0.257 |
| Random Effects | |||
| σ2 | 1117.65 | ||
| Ï„00 ID | 866.19 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 509 | ||
| Marginal R2 / Conditional R2 | 0.014 / 0.445 | ||
parameters::standardise_parameters(Mood_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.20 | [-0.07, 0.48]
## GroupB_Controls | -0.07 | [-0.40, 0.27]
## GroupC_Intervention | -0.24 | [-0.58, 0.10]
## TimeC_W1_mood_mean | -0.33 | [-0.63, -0.03]
## GroupB_Controls:TimeC_W1_mood_mean | 0.19 | [-0.18, 0.55]
## GroupC_Intervention:TimeC_W1_mood_mean | 0.21 | [-0.15, 0.57]plot_model(Mood_MEM_B1W, type = "int")
Baseline to 1M
Mood_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_mood_mean", "D_M1_mood_mean")## Formatting tables as needed
Mood_B1M_long <- Mood_B1M %>%
pivot_longer(cols = c("A_PRE_mood_mean", "D_M1_mood_mean"),
names_to = "Time",
values_to = "Mood_Score")Mood_MEM_B1M <- lmer(Mood_Score ~ Group * Time + (1|ID), data = Mood_B1M_long, REML = TRUE)
summary(Mood_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mood_Score ~ Group * Time + (1 | ID)
## Data: Mood_B1M_long
##
## REML criterion at convergence: 5011.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91923 -0.51813 0.07265 0.59296 2.42299
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 893.6 29.89
## Residual 1156.5 34.01
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 40.300 6.403 411.735 6.294
## GroupB_Controls -2.984 7.768 411.735 -0.384
## GroupC_Intervention -10.669 7.804 411.735 -1.367
## TimeD_M1_mood_mean -20.875 7.188 246.670 -2.904
## GroupB_Controls:TimeD_M1_mood_mean 14.198 8.688 245.758 1.634
## GroupC_Intervention:TimeD_M1_mood_mean 14.331 8.732 245.852 1.641
## Pr(>|t|)
## (Intercept) 7.95e-10 ***
## GroupB_Controls 0.70108
## GroupC_Intervention 0.17236
## TimeD_M1_mood_mean 0.00402 **
## GroupB_Controls:TimeD_M1_mood_mean 0.10351
## GroupC_Intervention:TimeD_M1_mood_mean 0.10205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_md_m -0.503 0.414 0.412
## GB_C:TD_M1_ 0.416 -0.504 -0.341 -0.827
## GC_I:TD_M1_ 0.414 -0.341 -0.504 -0.823 0.681anova (Mood_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2302.4 1151.2 2 260.11 0.9954 0.3709746
## Time 13435.3 13435.3 1 245.36 11.6170 0.0007637 ***
## Group:Time 3690.3 1845.2 2 245.06 1.5954 0.2049198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(Mood_MEM_B1M)| Â | Mood Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 40.30 | 27.72 – 52.88 | <0.001 |
| Group [B_Controls] | -2.98 | -18.25 – 12.28 | 0.701 |
| Group [C_Intervention] | -10.67 | -26.00 – 4.67 | 0.172 |
| Time [D_M1_mood_mean] | -20.87 | -35.00 – -6.75 | 0.004 |
|
Group [B_Controls] × Time [D_M1_mood_mean] |
14.20 | -2.87 – 31.27 | 0.103 |
|
Group [C_Intervention] × Time [D_M1_mood_mean] |
14.33 | -2.83 – 31.49 | 0.101 |
| Random Effects | |||
| σ2 | 1156.52 | ||
| Ï„00 ID | 893.64 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.447 | ||
parameters::standardise_parameters(Mood_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.22 | [-0.06, 0.49]
## GroupB_Controls | -0.07 | [-0.40, 0.27]
## GroupC_Intervention | -0.23 | [-0.57, 0.10]
## TimeD_M1_mood_mean | -0.46 | [-0.77, -0.15]
## GroupB_Controls:TimeD_M1_mood_mean | 0.31 | [-0.06, 0.69]
## GroupC_Intervention:TimeD_M1_mood_mean | 0.31 | [-0.06, 0.69]plot_model(Mood_MEM_B1M, type = "int")
Hypothesis 3
# Baseline to 1W/1M changes (creating new columns)
changeinvariables <- mutate(Full_data_all,
IUS_BP_change = B_POST_IUS_total - A_PRE_IUS_total,
IUS_B1W_change = C_W1_IUS_total - A_PRE_IUS_total,
IUS_B1M_change = D_M1_IUS_total - A_PRE_IUS_total,
PHQ_B1W_change = C_W1_PHQ_total - A_PRE_PHQ_total,
PHQ_B1M_change = D_M1_PHQ_total - A_PRE_PHQ_total,
GAD_B1W_change = C_W1_GAD_total - A_PRE_GAD_total,
GAD_B1M_change = D_M1_GAD_total - A_PRE_GAD_total,
Mood_BP_change = B_POST_mood_mean - A_PRE_mood_mean,
Mood_B1W_change = C_W1_mood_mean - A_PRE_mood_mean,
Mood_B1M_change = D_M1_mood_mean - A_PRE_mood_mean)# Separating out each group
Intervention_group <- changeinvariables %>%
filter(Group == "C_Intervention")
Psychoed_group <- changeinvariables %>%
filter(Group == "B_Controls")
ECs_group <- changeinvariables %>%
filter(Group == "A_ECs")cor.test(Intervention_group$IUS_B1W_change, Intervention_group$PHQ_B1W_change)##
## Pearson's product-moment correlation
##
## data: Intervention_group$IUS_B1W_change and Intervention_group$PHQ_B1W_change
## t = 0.31487, df = 98, p-value = 0.7535
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1656616 0.2267929
## sample estimates:
## cor
## 0.03179099cor.test(Psychoed_group$IUS_B1W_change, Psychoed_group$PHQ_B1W_change)##
## Pearson's product-moment correlation
##
## data: Psychoed_group$IUS_B1W_change and Psychoed_group$PHQ_B1W_change
## t = 2.3716, df = 101, p-value = 0.01961
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03783342 0.40519116
## sample estimates:
## cor
## 0.2296765cor.test(ECs_group$IUS_B1W_change, ECs_group$PHQ_B1W_change)##
## Pearson's product-moment correlation
##
## data: ECs_group$IUS_B1W_change and ECs_group$PHQ_B1W_change
## t = 1.0818, df = 46, p-value = 0.285
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1325568 0.4227263
## sample estimates:
## cor
## 0.1575103H3: IUS mediating change in PHQ over time between groups
Full sample at 1 week: Group to change in PHQ via change in IUS
Mediation.PHQchange.1W <-
'#regressions
PHQ_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
PHQ_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.PHQ.1W <- sem(Mediation.PHQchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1W_change ~
## Group (c1) -0.096 0.086 -1.108 0.268 -0.096 -0.071
## IUS_B1W_change ~
## Group (a1) -0.374 0.076 -4.913 0.000 -0.374 -0.278
## PHQ_B1W_change ~
## IUS_B1W_c (b1) 0.128 0.076 1.686 0.092 0.128 0.128
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.211 0.190 1.109 0.268 0.211 0.211
## .IUS_B1W_change 0.826 0.161 5.135 0.000 0.826 0.827
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.970 0.124 7.850 0.000 0.970 0.974
## .IUS_B1W_change 0.920 0.116 7.943 0.000 0.920 0.923
##
## R-Square:
## Estimate
## PHQ_B1W_change 0.026
## IUS_B1W_change 0.077
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.048 0.029 -1.665 0.096 -0.048 -0.036
## direct -0.096 0.086 -1.108 0.268 -0.096 -0.071
## total -0.144 0.082 -1.754 0.079 -0.144 -0.107Intervention group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.intervention.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.intervention.1W <- sem(Mediation.PHQ.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.110 0.016 6.983 0.000 0.110 0.621
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.013 0.017 0.760 0.447 0.013 0.073
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.053 0.080 0.667 0.505 0.053 0.054
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.177 0.145 -8.094 0.000 -1.177 -1.187
## .IUS_B1W_change -0.138 0.226 -0.611 0.541 -0.138 -0.139
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.596 0.103 5.810 0.000 0.596 0.606
## .IUS_B1W_change 0.985 0.201 4.900 0.000 0.985 0.995
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.394
## IUS_B1W_change 0.005
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.001 0.001 0.540 0.589 0.001 0.004
## direct 0.110 0.016 6.983 0.000 0.110 0.621
## total 0.111 0.016 7.040 0.000 0.111 0.625Psychoeducation group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.psychoed.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.psychoed.1W <- sem(Mediation.PHQ.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.123 0.012 10.659 0.000 0.123 0.758
## IUS_B1W_change ~
## A_PRE_PHQ (a1) -0.012 0.015 -0.824 0.410 -0.012 -0.075
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.142 0.073 1.956 0.050 0.142 0.143
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.169 0.097 -12.090 0.000 -1.169 -1.180
## .IUS_B1W_change 0.115 0.163 0.706 0.480 0.115 0.115
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.413 0.071 5.828 0.000 0.413 0.421
## .IUS_B1W_change 0.985 0.156 6.298 0.000 0.985 0.994
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.579
## IUS_B1W_change 0.006
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.002 0.002 -0.745 0.457 -0.002 -0.011
## direct 0.123 0.012 10.659 0.000 0.123 0.758
## total 0.121 0.012 10.122 0.000 0.121 0.748EC group at 1 week: pre PHQ to 1 week PHQ via change in IU
Mediation.PHQ.ECs.1W <-
'#regressions
C_W1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1W_change ~ a1 * A_PRE_PHQ_total
C_W1_PHQ_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.ECs.1W <- sem(Mediation.PHQ.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.126 0.010 13.005 0.000 0.126 0.736
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.050 0.017 2.900 0.004 0.050 0.287
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.182 0.075 2.431 0.015 0.182 0.183
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.269 0.111 -11.393 0.000 -1.269 -1.295
## .IUS_B1W_change -0.499 0.165 -3.017 0.003 -0.499 -0.504
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.333 0.093 3.582 0.000 0.333 0.347
## .IUS_B1W_change 0.896 0.187 4.789 0.000 0.896 0.918
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.653
## IUS_B1W_change 0.082
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.009 0.004 2.208 0.027 0.009 0.053
## direct 0.126 0.010 13.005 0.000 0.126 0.736
## total 0.135 0.009 14.812 0.000 0.135 0.789Full sample at 1 month: Group to change in PHQ via change in IUS
Mediation.PHQchange.1M <-
'#regressions
PHQ_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
PHQ_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.PHQ.1M <- sem(Mediation.PHQchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1M_change ~
## Group (c1) -0.174 0.087 -1.994 0.046 -0.174 -0.129
## IUS_B1M_change ~
## Group (a1) -0.413 0.076 -5.465 0.000 -0.413 -0.307
## PHQ_B1M_change ~
## IUS_B1M_c (b1) 0.237 0.087 2.717 0.007 0.237 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.383 0.196 1.950 0.051 0.383 0.383
## .IUS_B1M_change 0.911 0.163 5.574 0.000 0.911 0.913
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.907 0.104 8.708 0.000 0.907 0.909
## .IUS_B1M_change 0.901 0.101 8.897 0.000 0.901 0.906
##
## R-Square:
## Estimate
## PHQ_B1M_change 0.091
## IUS_B1M_change 0.094
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.098 0.040 -2.426 0.015 -0.098 -0.073
## direct -0.174 0.087 -1.994 0.046 -0.174 -0.129
## total -0.272 0.078 -3.464 0.001 -0.272 -0.202Intervention group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.intervention.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.intervention.1M <- sem(Mediation.PHQ.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.106 0.012 9.153 0.000 0.106 0.602
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.003 0.017 -0.174 0.862 -0.003 -0.017
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.162 0.102 1.593 0.111 0.162 0.163
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.140 0.140 -8.166 0.000 -1.140 -1.156
## .IUS_B1M_change 0.033 0.217 0.151 0.880 0.033 0.033
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.597 0.103 5.797 0.000 0.597 0.614
## .IUS_B1M_change 0.988 0.152 6.518 0.000 0.988 1.000
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.386
## IUS_B1M_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.003 -0.169 0.866 -0.000 -0.003
## direct 0.106 0.012 9.153 0.000 0.106 0.602
## total 0.106 0.012 8.632 0.000 0.106 0.599Psychoeducation group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.psychoed.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.psychoed.1M <- sem(Mediation.PHQ.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.086 0.015 5.885 0.000 0.086 0.529
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.015 0.016 -0.914 0.360 -0.015 -0.091
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.213 0.116 1.839 0.066 0.213 0.213
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -0.795 0.143 -5.559 0.000 -0.795 -0.802
## .IUS_B1M_change 0.134 0.172 0.778 0.437 0.134 0.134
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.685 0.097 7.039 0.000 0.685 0.696
## .IUS_B1M_change 0.980 0.174 5.636 0.000 0.980 0.992
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.304
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.004 -0.785 0.433 -0.003 -0.019
## direct 0.086 0.015 5.885 0.000 0.086 0.529
## total 0.082 0.015 5.494 0.000 0.082 0.509EC group at 1 month: pre PHQ to 1 month PHQ via change in IU
Mediation.PHQ.ECs.1M <-
'#regressions
D_M1_PHQ_total ~ c1 * A_PRE_PHQ_total
IUS_B1M_change ~ a1 * A_PRE_PHQ_total
D_M1_PHQ_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
PHQ.IUS.ECs.1M <- sem(Mediation.PHQ.ECs.1M, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(PHQ.IUS.ECs.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.120 0.014 8.357 0.000 0.120 0.699
## IUS_B1M_change ~
## A_PRE_PHQ (a1) 0.029 0.023 1.279 0.201 0.029 0.166
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.230 0.081 2.839 0.005 0.230 0.232
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.152 0.181 -6.352 0.000 -1.152 -1.171
## .IUS_B1M_change -0.280 0.261 -1.075 0.282 -0.280 -0.284
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.391 0.084 4.647 0.000 0.391 0.404
## .IUS_B1M_change 0.950 0.182 5.209 0.000 0.950 0.972
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.596
## IUS_B1M_change 0.028
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.007 0.005 1.212 0.226 0.007 0.039
## direct 0.120 0.014 8.357 0.000 0.120 0.699
## total 0.127 0.014 8.840 0.000 0.127 0.737H3: IUS mediating change in GAD over time between groups
Full sample at 1 week: Group to change in GAD via change in IUS
Mediation.GADchange.1W <-
'#regressions
GAD_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
GAD_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1W <- sem(Mediation.GADchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1W_change ~
## Group (c1) -0.059 0.083 -0.713 0.476 -0.059 -0.044
## IUS_B1W_change ~
## Group (a1) -0.374 0.076 -4.913 0.000 -0.374 -0.278
## GAD_B1W_change ~
## IUS_B1W_c (b1) 0.212 0.082 2.592 0.010 0.212 0.212
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.130 0.178 0.731 0.465 0.130 0.130
## .IUS_B1W_change 0.826 0.161 5.135 0.000 0.826 0.827
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.944 0.138 6.823 0.000 0.944 0.948
## .IUS_B1W_change 0.920 0.116 7.943 0.000 0.920 0.923
##
## R-Square:
## Estimate
## GAD_B1W_change 0.052
## IUS_B1W_change 0.077
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.079 0.036 -2.235 0.025 -0.079 -0.059
## direct -0.059 0.083 -0.713 0.476 -0.059 -0.044
## total -0.139 0.079 -1.759 0.079 -0.139 -0.103Intervention group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.intervention.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.intervention.1W <- sem(Mediation.GAD.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.119 0.015 8.001 0.000 0.119 0.652
## IUS_B1W_change ~
## A_PRE_GAD (a1) 0.001 0.019 0.038 0.970 0.001 0.004
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.083 0.095 0.879 0.379 0.083 0.083
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.100 0.117 -9.383 0.000 -1.100 -1.109
## .IUS_B1W_change -0.007 0.219 -0.031 0.975 -0.007 -0.007
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.558 0.112 4.980 0.000 0.558 0.567
## .IUS_B1W_change 0.990 0.205 4.834 0.000 0.990 1.000
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.433
## IUS_B1W_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.000 0.002 0.039 0.969 0.000 0.000
## direct 0.119 0.015 8.001 0.000 0.119 0.652
## total 0.119 0.015 7.955 0.000 0.119 0.652Psychoeducation group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.psychoed.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.psychoed.1W <- sem(Mediation.GAD.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.127 0.011 11.794 0.000 0.127 0.740
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.014 0.015 -0.890 0.373 -0.014 -0.079
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.200 0.054 3.744 0.000 0.200 0.201
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.088 0.102 -10.670 0.000 -1.088 -1.099
## .IUS_B1W_change 0.116 0.163 0.715 0.474 0.116 0.117
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.427 0.087 4.893 0.000 0.427 0.436
## .IUS_B1W_change 0.984 0.156 6.291 0.000 0.984 0.994
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.564
## IUS_B1W_change 0.006
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.003 -0.881 0.378 -0.003 -0.016
## direct 0.127 0.011 11.794 0.000 0.127 0.740
## total 0.124 0.011 10.851 0.000 0.124 0.724EC group at 1 week: pre GAD to 1 week GAD via change in IU
Mediation.GAD.ECs.1W <-
'#regressions
C_W1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1W_change ~ a1 * A_PRE_GAD_total
C_W1_GAD_total ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.ECs.1W <- sem(Mediation.GAD.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 22 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.156 0.014 11.020 0.000 0.156 0.765
## IUS_B1W_change ~
## A_PRE_GAD (a1) 0.031 0.024 1.281 0.200 0.031 0.151
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.230 0.068 3.390 0.001 0.230 0.232
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.261 0.110 -11.446 0.000 -1.261 -1.288
## .IUS_B1W_change -0.252 0.208 -1.207 0.227 -0.252 -0.254
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.294 0.066 4.479 0.000 0.294 0.307
## .IUS_B1W_change 0.956 0.202 4.732 0.000 0.956 0.977
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.693
## IUS_B1W_change 0.023
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.007 0.006 1.279 0.201 0.007 0.035
## direct 0.156 0.014 11.020 0.000 0.156 0.765
## total 0.163 0.014 11.286 0.000 0.163 0.800Full sample at 1 month: Group to change in GAD via change in IUS
Mediation.GADchange.1M <-
'#regressions
GAD_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
GAD_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1M <- sem(Mediation.GADchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1M_change ~
## Group (c1) -0.147 0.093 -1.587 0.113 -0.147 -0.109
## IUS_B1M_change ~
## Group (a1) -0.413 0.076 -5.463 0.000 -0.413 -0.307
## GAD_B1M_change ~
## IUS_B1M_c (b1) 0.338 0.091 3.714 0.000 0.338 0.337
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.320 0.217 1.473 0.141 0.320 0.319
## .IUS_B1M_change 0.909 0.163 5.566 0.000 0.909 0.912
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.854 0.104 8.239 0.000 0.854 0.852
## .IUS_B1M_change 0.901 0.101 8.900 0.000 0.901 0.906
##
## R-Square:
## Estimate
## GAD_B1M_change 0.148
## IUS_B1M_change 0.094
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.140 0.046 -3.048 0.002 -0.140 -0.103
## direct -0.147 0.093 -1.587 0.113 -0.147 -0.109
## total -0.287 0.084 -3.405 0.001 -0.287 -0.212Intervention group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.intervention.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.intervention.1M <- sem(Mediation.GAD.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.120 0.013 9.558 0.000 0.120 0.664
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.004 0.019 -0.230 0.818 -0.004 -0.023
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.215 0.099 2.174 0.030 0.215 0.217
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.131 0.114 -9.950 0.000 -1.131 -1.149
## .IUS_B1M_change 0.041 0.203 0.203 0.839 0.041 0.041
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.503 0.084 5.955 0.000 0.503 0.520
## .IUS_B1M_change 0.988 0.152 6.481 0.000 0.988 0.999
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.480
## IUS_B1M_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.004 -0.222 0.824 -0.001 -0.005
## direct 0.120 0.013 9.558 0.000 0.120 0.664
## total 0.119 0.013 9.117 0.000 0.119 0.658Psychoeducation group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.psychoed.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.psychoed.1M <- sem(Mediation.GAD.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.098 0.013 7.472 0.000 0.098 0.567
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.008 0.019 -0.453 0.651 -0.008 -0.049
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.395 0.110 3.580 0.000 0.395 0.394
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -0.819 0.114 -7.170 0.000 -0.819 -0.821
## .IUS_B1M_change 0.063 0.174 0.362 0.717 0.063 0.063
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.543 0.080 6.825 0.000 0.543 0.546
## .IUS_B1M_change 0.985 0.175 5.617 0.000 0.985 0.998
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.454
## IUS_B1M_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.008 -0.437 0.662 -0.003 -0.019
## direct 0.098 0.013 7.472 0.000 0.098 0.567
## total 0.095 0.016 5.996 0.000 0.095 0.547EC group at 1 month: pre GAD to 1 month GAD via change in IU
Mediation.GAD.ECs.1M <-
'#regressions
D_M1_GAD_total ~ c1 * A_PRE_GAD_total
IUS_B1M_change ~ a1 * A_PRE_GAD_total
D_M1_GAD_total ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
GAD.IUS.ECs.1M <- sem(Mediation.GAD.ECs.1M, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(GAD.IUS.ECs.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.117 0.022 5.424 0.000 0.117 0.578
## IUS_B1M_change ~
## A_PRE_GAD (a1) 0.019 0.027 0.700 0.484 0.019 0.091
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.227 0.074 3.059 0.002 0.227 0.231
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -0.914 0.251 -3.636 0.000 -0.914 -0.939
## .IUS_B1M_change -0.150 0.261 -0.576 0.565 -0.150 -0.152
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.557 0.180 3.086 0.002 0.557 0.588
## .IUS_B1M_change 0.968 0.192 5.044 0.000 0.968 0.992
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.412
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.004 0.006 0.682 0.495 0.004 0.021
## direct 0.117 0.022 5.424 0.000 0.117 0.578
## total 0.121 0.021 5.811 0.000 0.121 0.599H3: IUS mediating change in mood over time between groups
Full sample at post: Group to change in mood via change in IUS
Mediation.Moodchange.post <-
'#regressions
Mood_BP_change ~ c1 * Group
IUS_BP_change ~ a1 * Group
Mood_BP_change ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.post <- sem(Mediation.Moodchange.post, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_BP_change ~
## Group (c1) 0.315 0.076 4.119 0.000 0.315 0.233
## IUS_BP_change ~
## Group (a1) -0.403 0.079 -5.103 0.000 -0.403 -0.299
## Mood_BP_change ~
## IUS_BP_ch (b1) -0.246 0.070 -3.524 0.000 -0.246 -0.246
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change -0.693 0.164 -4.220 0.000 -0.693 -0.694
## .IUS_BP_change 0.887 0.165 5.388 0.000 0.887 0.889
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change 0.847 0.143 5.912 0.000 0.847 0.850
## .IUS_BP_change 0.906 0.114 7.947 0.000 0.906 0.910
##
## R-Square:
## Estimate
## Mood_BP_change 0.150
## IUS_BP_change 0.090
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.099 0.032 3.152 0.002 0.099 0.074
## direct 0.315 0.076 4.119 0.000 0.315 0.233
## total 0.414 0.072 5.736 0.000 0.414 0.307Intervention group at post: pre mood to post mood via change in IU
Mediation.Mood.intervention.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.post <- sem(Mediation.Mood.intervention.post, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.014 0.003 4.726 0.000 0.014 0.607
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.002 0.002 0.939 0.348 0.002 0.098
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.178 0.080 -2.224 0.026 -0.178 -0.178
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.416 0.148 -2.809 0.005 -0.416 -0.418
## .IUS_BP_change -0.066 0.108 -0.606 0.545 -0.066 -0.066
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.614 0.128 4.807 0.000 0.614 0.621
## .IUS_BP_change 0.980 0.182 5.374 0.000 0.980 0.990
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.379
## IUS_BP_change 0.010
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.827 0.408 -0.000 -0.017
## direct 0.014 0.003 4.726 0.000 0.014 0.607
## total 0.014 0.003 4.731 0.000 0.014 0.589Psychoeducation group at post: pre mood to post mood via change in IU
Mediation.Mood.psychoed.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.post <- sem(Mediation.Mood.psychoed.post, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.016 0.002 7.911 0.000 0.016 0.695
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.002 0.002 0.698 0.485 0.002 0.067
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.241 0.069 -3.506 0.000 -0.241 -0.241
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.589 0.119 -4.939 0.000 -0.589 -0.592
## .IUS_BP_change -0.057 0.138 -0.412 0.681 -0.057 -0.057
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.477 0.083 5.756 0.000 0.477 0.482
## .IUS_BP_change 0.986 0.196 5.026 0.000 0.986 0.996
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.518
## IUS_BP_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.647 0.518 -0.000 -0.016
## direct 0.016 0.002 7.911 0.000 0.016 0.695
## total 0.015 0.002 7.039 0.000 0.015 0.679EC group at post: pre mood to post mood via change in IU
Mediation.Mood.ECs.post <-
'#regressions
B_POST_mood_mean ~ c1 * A_PRE_mood_mean
IUS_BP_change ~ a1 * A_PRE_mood_mean
B_POST_mood_mean ~ b1*IUS_BP_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.ECs.post <- sem(Mediation.Mood.ECs.post, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.ECs.post, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 1
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.022 0.002 13.400 0.000 0.022 0.868
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.001 0.003 0.236 0.814 0.001 0.025
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.063 0.060 -1.052 0.293 -0.063 -0.063
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.895 0.104 -8.601 0.000 -0.895 -0.904
## .IUS_BP_change -0.026 0.178 -0.147 0.883 -0.026 -0.026
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.240 0.063 3.801 0.000 0.240 0.245
## .IUS_BP_change 0.979 0.226 4.336 0.000 0.979 0.999
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.755
## IUS_BP_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.231 0.817 -0.000 -0.002
## direct 0.022 0.002 13.400 0.000 0.022 0.868
## total 0.022 0.002 13.364 0.000 0.022 0.866Full sample at 1 week: Group to change in mood via change in IUS
Mediation.Moodchange.1W <-
'#regressions
Mood_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
Mood_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.1W <- sem(Mediation.Moodchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_B1W_change ~
## Group (c1) 0.020 0.088 0.223 0.823 0.020 0.015
## IUS_B1W_change ~
## Group (a1) -0.374 0.076 -4.913 0.000 -0.374 -0.278
## Mood_B1W_change ~
## IUS_B1W_c (b1) -0.170 0.076 -2.246 0.025 -0.170 -0.171
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang -0.042 0.202 -0.207 0.836 -0.042 -0.042
## .IUS_B1W_change 0.826 0.161 5.135 0.000 0.826 0.827
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang 0.966 0.121 7.987 0.000 0.966 0.969
## .IUS_B1W_change 0.920 0.116 7.943 0.000 0.920 0.923
##
## R-Square:
## Estimate
## Mood_B1W_chang 0.031
## IUS_B1W_change 0.077
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.064 0.030 2.158 0.031 0.064 0.047
## direct 0.020 0.088 0.223 0.823 0.020 0.015
## total 0.084 0.081 1.035 0.301 0.084 0.062Intervention group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.intervention.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.1W <- sem(Mediation.Mood.intervention.1W, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.011 0.002 4.421 0.000 0.011 0.457
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.000 0.002 0.080 0.936 0.000 0.008
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.114 0.097 -1.173 0.241 -0.114 -0.115
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.322 0.109 -2.952 0.003 -0.322 -0.325
## .IUS_B1W_change -0.006 0.094 -0.062 0.951 -0.006 -0.006
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.768 0.155 4.947 0.000 0.768 0.779
## .IUS_B1W_change 0.990 0.205 4.822 0.000 0.990 1.000
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.221
## IUS_B1W_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.080 0.936 -0.000 -0.001
## direct 0.011 0.002 4.421 0.000 0.011 0.457
## total 0.011 0.002 4.294 0.000 0.011 0.456Psychoeducation group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.psychoed.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.1W <- sem(Mediation.Mood.psychoed.1W, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.478 0.000 0.010 0.448
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.003 0.002 1.250 0.211 0.003 0.134
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.165 0.100 -1.648 0.099 -0.165 -0.165
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.376 0.135 -2.776 0.006 -0.376 -0.379
## .IUS_B1W_change -0.112 0.154 -0.730 0.465 -0.112 -0.113
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.781 0.126 6.223 0.000 0.781 0.792
## .IUS_B1W_change 0.972 0.150 6.481 0.000 0.972 0.982
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.208
## IUS_B1W_change 0.018
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.001 -0.903 0.366 -0.001 -0.022
## direct 0.010 0.002 4.478 0.000 0.010 0.448
## total 0.010 0.002 4.067 0.000 0.010 0.426EC group at 1 week: pre mood to 1 week mood via change in IU
Mediation.Mood.ECs.1W <-
'#regressions
C_W1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1W_change ~ a1 * A_PRE_mood_mean
C_W1_mood_mean ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.ECs.1W <- sem(Mediation.Mood.ECs.1W, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.ECs.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_W1_mood_mean ~
## A_PRE_md_ (c1) 0.011 0.003 3.198 0.001 0.011 0.435
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.006 0.003 2.161 0.031 0.006 0.249
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.042 0.130 -0.320 0.749 -0.042 -0.042
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.447 0.181 -2.475 0.013 -0.447 -0.454
## .IUS_B1W_change -0.257 0.179 -1.431 0.152 -0.257 -0.260
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.795 0.129 6.163 0.000 0.795 0.818
## .IUS_B1W_change 0.916 0.188 4.880 0.000 0.916 0.938
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.182
## IUS_B1W_change 0.062
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.313 0.754 -0.000 -0.010
## direct 0.011 0.003 3.198 0.001 0.011 0.435
## total 0.011 0.003 3.237 0.001 0.011 0.425Full sample at 1 month: Group to change in mood via change in IUS
Mediation.Moodchange.1M <-
'#regressions
Mood_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
Mood_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.Mood.1M <- sem(Mediation.Moodchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.Mood.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mood_B1M_change ~
## Group (c1) 0.025 0.092 0.269 0.788 0.025 0.018
## IUS_B1M_change ~
## Group (a1) -0.416 0.076 -5.495 0.000 -0.416 -0.308
## Mood_B1M_change ~
## IUS_B1M_c (b1) -0.244 0.085 -2.857 0.004 -0.244 -0.244
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang -0.053 0.209 -0.255 0.798 -0.053 -0.053
## .IUS_B1M_change 0.914 0.163 5.593 0.000 0.914 0.916
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.935 0.104 9.030 0.000 0.935 0.937
## .IUS_B1M_change 0.902 0.101 8.898 0.000 0.902 0.905
##
## R-Square:
## Estimate
## Mood_B1M_chang 0.063
## IUS_B1M_change 0.095
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.101 0.038 2.655 0.008 0.101 0.075
## direct 0.025 0.092 0.269 0.788 0.025 0.018
## total 0.126 0.083 1.524 0.127 0.126 0.094Intervention group at 1 month: pre mood to 1 month mood via change in IU
Mediation.Mood.intervention.1M <-
'#regressions
D_M1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1M_change ~ a1 * A_PRE_mood_mean
D_M1_mood_mean ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.intervention.1M <- sem(Mediation.Mood.intervention.1M, data=Intervention_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.intervention.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 19 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.147 0.000 0.010 0.418
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.002 0.002 1.034 0.301 0.002 0.099
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.183 0.092 -1.974 0.048 -0.183 -0.183
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.291 0.116 -2.517 0.012 -0.291 -0.293
## .IUS_B1M_change -0.073 0.112 -0.658 0.510 -0.073 -0.074
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.793 0.140 5.682 0.000 0.793 0.807
## .IUS_B1M_change 0.979 0.153 6.408 0.000 0.979 0.990
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.193
## IUS_B1M_change 0.010
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.845 0.398 -0.000 -0.018
## direct 0.010 0.002 4.147 0.000 0.010 0.418
## total 0.009 0.002 3.801 0.000 0.009 0.400Psychoeducation group at 1 month: pre mood to 1 month mood via change in IU
Mediation.Mood.psychoed.1M <-
'#regressions
D_M1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1M_change ~ a1 * A_PRE_mood_mean
D_M1_mood_mean ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.psychoed.1M <- sem(Mediation.Mood.psychoed.1M, data=Psychoed_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.psychoed.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.916 0.000 0.010 0.438
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.002 0.002 0.827 0.408 0.002 0.089
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.215 0.107 -2.007 0.045 -0.215 -0.216
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.359 0.123 -2.921 0.003 -0.359 -0.362
## .IUS_B1M_change -0.078 0.140 -0.555 0.579 -0.078 -0.078
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.766 0.092 8.296 0.000 0.766 0.778
## .IUS_B1M_change 0.980 0.178 5.512 0.000 0.980 0.992
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.222
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.662 0.508 -0.000 -0.019
## direct 0.010 0.002 4.916 0.000 0.010 0.438
## total 0.009 0.002 4.195 0.000 0.009 0.419EC group at 1 month: pre mood to 1 month mood via change in IU
Mediation.Mood.EC.1M <-
'#regressions
D_M1_mood_mean ~ c1 * A_PRE_mood_mean
IUS_B1M_change ~ a1 * A_PRE_mood_mean
D_M1_mood_mean ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
Mood.IUS.EC.1M <- sem(Mediation.Mood.EC.1M, data=ECs_group, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(Mood.IUS.EC.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 50
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.016 0.002 7.150 0.000 0.016 0.621
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.003 0.004 0.706 0.480 0.003 0.102
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.105 0.104 -1.007 0.314 -0.105 -0.104
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.663 0.148 -4.492 0.000 -0.663 -0.663
## .IUS_B1M_change -0.103 0.221 -0.468 0.640 -0.103 -0.105
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.616 0.122 5.039 0.000 0.616 0.616
## .IUS_B1M_change 0.967 0.202 4.796 0.000 0.967 0.990
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.384
## IUS_B1M_change 0.010
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.588 0.557 -0.000 -0.011
## direct 0.016 0.002 7.150 0.000 0.016 0.621
## total 0.016 0.002 6.564 0.000 0.016 0.611Exploratory analyses
E1: Growth mindsets moderating the association between group and PHQ changes across time
# 1 week
moderation_GM_PHQ_1W <- lm(PHQ_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1W)##
## Call:
## lm(formula = PHQ_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1562 -2.2737 0.4147 2.7263 12.4817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.2237 1.4201 -1.566 0.1187
## GroupB_Controls 0.5795 1.7734 0.327 0.7441
## GroupC_Intervention 1.8281 1.7812 1.026 0.3058
## A_PRE_GM 0.7632 0.4597 1.660 0.0982 .
## GroupB_Controls:A_PRE_GM -0.5337 0.5529 -0.965 0.3354
## GroupC_Intervention:A_PRE_GM -1.1252 0.5673 -1.983 0.0485 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.481 on 245 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02936, Adjusted R-squared: 0.009554
## F-statistic: 1.482 on 5 and 245 DF, p-value: 0.1961anova(moderation_GM_PHQ_1W)## Analysis of Variance Table
##
## Response: PHQ_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 58.5 29.234 1.4560 0.2352
## A_PRE_GM 1 6.3 6.310 0.3143 0.5756
## Group:A_PRE_GM 2 84.0 42.016 2.0926 0.1256
## Residuals 245 4919.1 20.078# 1 month
moderation_GM_PHQ_1M <- lm(PHQ_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1M)##
## Call:
## lm(formula = PHQ_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.9147 -2.8178 -0.2693 2.9100 19.5368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3893 1.8426 0.211 0.833
## GroupB_Controls -3.2169 2.3213 -1.386 0.167
## GroupC_Intervention -2.5702 2.2870 -1.124 0.262
## A_PRE_GM 0.1475 0.6019 0.245 0.807
## GroupB_Controls:A_PRE_GM 0.4009 0.7291 0.550 0.583
## GroupC_Intervention:A_PRE_GM -0.2152 0.7397 -0.291 0.771
##
## Residual standard error: 5.538 on 222 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.04929, Adjusted R-squared: 0.02788
## F-statistic: 2.302 on 5 and 222 DF, p-value: 0.04579anova(moderation_GM_PHQ_1M)## Analysis of Variance Table
##
## Response: PHQ_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 295.9 147.948 4.8242 0.008894 **
## A_PRE_GM 1 23.5 23.462 0.7650 0.382702
## Group:A_PRE_GM 2 33.6 16.824 0.5486 0.578536
## Residuals 222 6808.2 30.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E1: Growth mindsets moderating the association between group and GAD changes across time
# 1 week
moderation_GM_GAD_1W <- lm(GAD_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1W)##
## Call:
## lm(formula = GAD_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.697 -1.974 0.186 2.062 14.731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4596 1.3623 -1.071 0.2850
## GroupB_Controls 2.3146 1.7013 1.360 0.1749
## GroupC_Intervention 1.3013 1.7088 0.762 0.4471
## A_PRE_GM 0.6368 0.4410 1.444 0.1500
## GroupB_Controls:A_PRE_GM -1.1092 0.5304 -2.091 0.0375 *
## GroupC_Intervention:A_PRE_GM -0.9231 0.5443 -1.696 0.0911 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.299 on 245 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.03314, Adjusted R-squared: 0.01341
## F-statistic: 1.68 on 5 and 245 DF, p-value: 0.1401anova(moderation_GM_GAD_1W)## Analysis of Variance Table
##
## Response: GAD_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 54.3 27.131 1.4683 0.2323
## A_PRE_GM 1 17.3 17.305 0.9366 0.3341
## Group:A_PRE_GM 2 83.6 41.801 2.2623 0.1063
## Residuals 245 4527.0 18.478# 1 month
moderation_GM_GAD_1M <- lm(GAD_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1M)##
## Call:
## lm(formula = GAD_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.3797 -2.6023 0.1837 2.5306 17.2648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3786 1.6842 0.225 0.822
## GroupB_Controls -1.5825 2.1219 -0.746 0.457
## GroupC_Intervention -1.2153 2.0904 -0.581 0.562
## A_PRE_GM 0.3566 0.5502 0.648 0.518
## GroupB_Controls:A_PRE_GM -0.2399 0.6668 -0.360 0.719
## GroupC_Intervention:A_PRE_GM -0.7035 0.6761 -1.041 0.299
##
## Residual standard error: 5.062 on 221 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.05464, Adjusted R-squared: 0.03325
## F-statistic: 2.554 on 5 and 221 DF, p-value: 0.02854anova(moderation_GM_GAD_1M)## Analysis of Variance Table
##
## Response: GAD_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 294.1 147.030 5.7385 0.003718 **
## A_PRE_GM 1 0.1 0.092 0.0036 0.952337
## Group:A_PRE_GM 2 33.1 16.547 0.6458 0.525220
## Residuals 221 5662.3 25.621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E1: Growth mindsets moderating the association between group and mood changes across time
# post
moderation_GM_mood_BP <- lm(Mood_BP_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_BP)##
## Call:
## lm(formula = Mood_BP_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.57 -19.14 -3.97 15.23 171.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.380 10.096 -0.830 0.40732
## GroupB_Controls 31.642 12.618 2.508 0.01278 *
## GroupC_Intervention 38.846 12.577 3.089 0.00223 **
## A_PRE_GM 3.051 3.289 0.928 0.35455
## GroupB_Controls:A_PRE_GM -4.098 3.954 -1.036 0.30097
## GroupC_Intervention:A_PRE_GM -3.409 4.037 -0.845 0.39913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.17 on 252 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1039, Adjusted R-squared: 0.08617
## F-statistic: 5.847 on 5 and 252 DF, p-value: 3.945e-05anova(moderation_GM_mood_BP)## Analysis of Variance Table
##
## Response: Mood_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 29097 14548.3 14.0609 1.625e-06 ***
## A_PRE_GM 1 0 0.0 0.0000 0.9988
## Group:A_PRE_GM 2 1150 575.1 0.5558 0.5743
## Residuals 252 260736 1034.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_GM_mood_1W <- lm(Mood_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_1W)##
## Call:
## lm(formula = Mood_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -185.320 -23.963 2.028 25.016 185.316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.2285 15.0894 -1.009 0.314
## GroupB_Controls 15.5416 18.8441 0.825 0.410
## GroupC_Intervention 16.4329 18.9320 0.868 0.386
## A_PRE_GM 0.1816 4.8849 0.037 0.970
## GroupB_Controls:A_PRE_GM -2.3073 5.8749 -0.393 0.695
## GroupC_Intervention:A_PRE_GM -2.5278 6.0285 -0.419 0.675
##
## Residual standard error: 47.61 on 244 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.00881, Adjusted R-squared: -0.0115
## F-statistic: 0.4338 on 5 and 244 DF, p-value: 0.8248anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 2952 1476.0 0.6511 0.5224
## A_PRE_GM 1 1516 1515.6 0.6686 0.4143
## Group:A_PRE_GM 2 449 224.5 0.0990 0.9057
## Residuals 244 553135 2266.9# 1 month
moderation_GM_mood_1W <- lm(Mood_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_1W)##
## Call:
## lm(formula = Mood_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -150.984 -28.262 4.266 26.821 183.538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.7643 16.1860 -1.839 0.0673 .
## GroupB_Controls 27.3570 20.3907 1.342 0.1811
## GroupC_Intervention 13.0556 20.0891 0.650 0.5164
## A_PRE_GM 3.0544 5.2872 0.578 0.5641
## GroupB_Controls:A_PRE_GM -4.2805 6.4042 -0.668 0.5046
## GroupC_Intervention:A_PRE_GM 0.4938 6.4979 0.076 0.9395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.65 on 222 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.02057, Adjusted R-squared: -0.001493
## F-statistic: 0.9323 on 5 and 222 DF, p-value: 0.4608anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 7881 3940.5 1.6652 0.1915
## A_PRE_GM 1 904 904.4 0.3822 0.5371
## Group:A_PRE_GM 2 2246 1122.9 0.4745 0.6228
## Residuals 222 525329 2366.3E2: Association between IUS and baseline functional impairment
# Total FI scale
PRE_IUS_FI_lm <- lm(A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
summary(PRE_IUS_FI_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.1561 -4.1561 0.3806 5.0026 15.3806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.1074 1.2521 23.25 <2e-16 ***
## A_PRE_FI_total 1.2927 0.1148 11.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.265 on 257 degrees of freedom
## Multiple R-squared: 0.3304, Adjusted R-squared: 0.3278
## F-statistic: 126.8 on 1 and 257 DF, p-value: < 2.2e-16anova(PRE_IUS_FI_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_FI_total 1 6692.6 6692.6 126.8 < 2.2e-16 ***
## Residuals 257 13565.1 52.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plot_lm_fi <- ggplot(data = Full_data_all, aes(x = A_PRE_IUS_total, y = A_PRE_FI_total)) +
geom_point() +
geom_smooth(method = "lm", color = "black")
print(Plot_lm_fi)## `geom_smooth()` using formula = 'y ~ x'
# Friends item
PRE_IUS_friends_lm <- lm(A_PRE_IUS_total ~ B_FI_friends, data = Full_data_all)
summary(PRE_IUS_friends_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_friends, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.7419 -5.1496 0.6416 6.0252 19.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.2760 1.3886 23.963 < 2e-16 ***
## B_FI_friends 3.2329 0.4657 6.942 3.16e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.159 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1584, Adjusted R-squared: 0.1551
## F-statistic: 48.2 on 1 and 256 DF, p-value: 3.163e-11anova(PRE_IUS_friends_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_friends 1 3208.3 3208.3 48.195 3.163e-11 ***
## Residuals 256 17041.8 66.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Strangers item
PRE_IUS_strangers_lm <- lm(A_PRE_IUS_total ~ B_FI_strangers, data = Full_data_all)
summary(PRE_IUS_strangers_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_strangers, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.857 -4.948 1.075 6.143 20.189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.7198 1.7331 20.033 < 2e-16 ***
## B_FI_strangers 2.0457 0.4489 4.557 8.03e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.548 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07504, Adjusted R-squared: 0.07142
## F-statistic: 20.77 on 1 and 256 DF, p-value: 8.034e-06anova(PRE_IUS_strangers_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_strangers 1 1517.6 1517.59 20.768 8.034e-06 ***
## Residuals 256 18707.0 73.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Work item
PRE_IUS_work_lm <- lm(A_PRE_IUS_total ~ B_FI_work, data = Full_data_all)
summary(PRE_IUS_work_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_work, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.9966 -4.3259 0.0034 5.8388 18.9211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.914 1.450 20.627 <2e-16 ***
## B_FI_work 4.082 0.451 9.051 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.705 on 243 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.2521, Adjusted R-squared: 0.249
## F-statistic: 81.92 on 1 and 243 DF, p-value: < 2.2e-16anova(PRE_IUS_work_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_work 1 4863.8 4863.8 81.92 < 2.2e-16 ***
## Residuals 243 14427.5 59.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Education item
PRE_IUS_education_lm <- lm(A_PRE_IUS_total ~ B_FI_education, data = Full_data_all)
summary(PRE_IUS_education_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_education, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.9221 -4.9864 0.0136 5.9976 18.0457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.8900 1.3789 23.853 < 2e-16 ***
## B_FI_education 3.0321 0.4192 7.233 5.79e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.153 on 249 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1736, Adjusted R-squared: 0.1703
## F-statistic: 52.32 on 1 and 249 DF, p-value: 5.793e-12anova(PRE_IUS_education_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_education 1 3478.3 3478.3 52.323 5.793e-12 ***
## Residuals 249 16552.6 66.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Hobbies item
PRE_IUS_hobbies_lm <- lm(A_PRE_IUS_total ~ B_FI_hobbies, data = Full_data_all)
summary(PRE_IUS_hobbies_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ B_FI_hobbies, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.3848 -5.5183 0.0813 5.4372 17.6152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.2068 1.2818 25.907 < 2e-16 ***
## B_FI_hobbies 3.1780 0.4149 7.659 3.85e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.022 on 256 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1864, Adjusted R-squared: 0.1832
## F-statistic: 58.66 on 1 and 256 DF, p-value: 3.853e-13anova(PRE_IUS_hobbies_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_FI_hobbies 1 3775.2 3775.2 58.662 3.853e-13 ***
## Residuals 256 16474.9 64.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E2: Change in functional impairment over time across groups
# Merging across timepoints
FI_alltimepoints <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total", "D_M1_FI_total")## Formatting table as needed
FI_alltimepoints_long <- FI_alltimepoints %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_alltimepoints_long, REML = TRUE)
summary(FI_MEM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_alltimepoints_long
##
## REML criterion at convergence: 3881
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4491 -0.5504 -0.0242 0.5101 3.7366
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.956 3.155
## Residual 6.141 2.478
## Number of obs: 740, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.860000 0.567386 426.967909 17.378
## GroupB_Controls 0.168302 0.688316 426.967908 0.245
## GroupC_Intervention 0.615728 0.691521 426.967908 0.890
## TimeC_W1_FI_total 0.068035 0.502695 478.593554 0.135
## TimeD_M1_FI_total -0.008774 0.518450 483.024020 -0.017
## GroupB_Controls:TimeC_W1_FI_total -0.259669 0.609119 478.641804 -0.426
## GroupC_Intervention:TimeC_W1_FI_total -1.041367 0.611891 478.402576 -1.702
## GroupB_Controls:TimeD_M1_FI_total -0.687018 0.628494 483.127556 -1.093
## GroupC_Intervention:TimeD_M1_FI_total -1.151707 0.630779 482.776453 -1.826
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.8070
## GroupC_Intervention 0.3738
## TimeC_W1_FI_total 0.8924
## TimeD_M1_FI_total 0.9865
## GroupB_Controls:TimeC_W1_FI_total 0.6701
## GroupC_Intervention:TimeC_W1_FI_total 0.0894 .
## GroupB_Controls:TimeD_M1_FI_total 0.2749
## GroupC_Intervention:TimeD_M1_FI_total 0.0685 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ TD_M1_ GB_C:TC GC_I:TC GB_C:TD
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_FI_t -0.431 0.355 0.353
## TmD_M1_FI_t -0.417 0.344 0.343 0.470
## GB_C:TC_W1_ 0.355 -0.431 -0.292 -0.825 -0.388
## GC_I:TC_W1_ 0.354 -0.292 -0.431 -0.822 -0.386 0.678
## GB_C:TD_M1_ 0.344 -0.418 -0.283 -0.388 -0.825 0.472 0.318
## GC_I:TD_M1_ 0.343 -0.283 -0.418 -0.386 -0.822 0.319 0.471 0.678anova (FI_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.379 0.1896 2 257.42 0.0309 0.96960
## Time 41.697 20.8486 2 481.96 3.3952 0.03434 *
## Group:Time 32.649 8.1623 4 481.87 1.3292 0.25799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.86 | 8.75 – 10.97 | <0.001 |
| Group [B_Controls] | 0.17 | -1.18 – 1.52 | 0.807 |
| Group [C_Intervention] | 0.62 | -0.74 – 1.97 | 0.374 |
| Time [C_W1_FI_total] | 0.07 | -0.92 – 1.05 | 0.892 |
| Time [D_M1_FI_total] | -0.01 | -1.03 – 1.01 | 0.987 |
|
Group [B_Controls] × Time [C_W1_FI_total] |
-0.26 | -1.46 – 0.94 | 0.670 |
|
Group [C_Intervention] × Time [C_W1_FI_total] |
-1.04 | -2.24 – 0.16 | 0.089 |
|
Group [B_Controls] × Time [D_M1_FI_total] |
-0.69 | -1.92 – 0.55 | 0.275 |
|
Group [C_Intervention] × Time [D_M1_FI_total] |
-1.15 | -2.39 – 0.09 | 0.068 |
| Random Effects | |||
| σ2 | 6.14 | ||
| Ï„00 ID | 9.96 | ||
| ICC | 0.62 | ||
| N ID | 259 | ||
| Observations | 740 | ||
| Marginal R2 / Conditional R2 | 0.009 / 0.622 | ||
parameters::standardise_parameters(FI_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.02 | [-0.26, 0.29]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.15 | [-0.18, 0.49]
## TimeC_W1_FI_total | 0.02 | [-0.23, 0.26]
## TimeD_M1_FI_total | -2.18e-03 | [-0.26, 0.25]
## GroupB_Controls:TimeC_W1_FI_total | -0.06 | [-0.36, 0.23]
## GroupC_Intervention:TimeC_W1_FI_total | -0.26 | [-0.56, 0.04]
## GroupB_Controls:TimeD_M1_FI_total | -0.17 | [-0.48, 0.14]
## GroupC_Intervention:TimeD_M1_FI_total | -0.29 | [-0.59, 0.02]Baseline to 1W
# Merging across timepoints
FI_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total")## Formatting table as needed
FI_B1W_long <- FI_B1W %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1W <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1W_long, REML = TRUE)
summary(FI_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1W_long
##
## REML criterion at convergence: 2740.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86590 -0.50210 -0.00798 0.48671 2.80124
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.039 3.007
## Residual 6.577 2.565
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.86000 0.55886 380.24977 17.643
## GroupB_Controls 0.16830 0.67797 380.24977 0.248
## GroupC_Intervention 0.61573 0.68113 380.24977 0.904
## TimeC_W1_FI_total 0.09842 0.52127 252.70546 0.189
## GroupB_Controls:TimeC_W1_FI_total -0.30400 0.63140 252.38770 -0.481
## GroupC_Intervention:TimeC_W1_FI_total -1.05347 0.63439 252.40409 -1.661
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.804
## GroupC_Intervention 0.367
## TimeC_W1_FI_total 0.850
## GroupB_Controls:TimeC_W1_FI_total 0.631
## GroupC_Intervention:TimeC_W1_FI_total 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_FI_t -0.452 0.372 0.370
## GB_C:TC_W1_ 0.373 -0.452 -0.306 -0.826
## GC_I:TC_W1_ 0.371 -0.306 -0.452 -0.822 0.678anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.2043 0.1022 2 256.87 0.0155 0.9846
## Time 14.0165 14.0165 1 252.24 2.1312 0.1456
## Group:Time 23.2052 11.6026 2 252.13 1.7642 0.1734sjPlot::tab_model(FI_MEM_B1W)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.86 | 8.76 – 10.96 | <0.001 |
| Group [B_Controls] | 0.17 | -1.16 – 1.50 | 0.804 |
| Group [C_Intervention] | 0.62 | -0.72 – 1.95 | 0.366 |
| Time [C_W1_FI_total] | 0.10 | -0.93 – 1.12 | 0.850 |
|
Group [B_Controls] × Time [C_W1_FI_total] |
-0.30 | -1.54 – 0.94 | 0.630 |
|
Group [C_Intervention] × Time [C_W1_FI_total] |
-1.05 | -2.30 – 0.19 | 0.097 |
| Random Effects | |||
| σ2 | 6.58 | ||
| Ï„00 ID | 9.04 | ||
| ICC | 0.58 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.006 / 0.581 | ||
parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.16 | [-0.18, 0.49]
## TimeC_W1_FI_total | 0.02 | [-0.23, 0.28]
## GroupB_Controls:TimeC_W1_FI_total | -0.08 | [-0.39, 0.24]
## GroupC_Intervention:TimeC_W1_FI_total | -0.27 | [-0.58, 0.05]plot_model(FI_MEM_B1W, type = "int")
Baseline to 1M
# Merging across timepoints
FI_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "D_M1_FI_total")## Formatting table as needed
FI_B1M_long <- FI_B1M %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1M <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1M_long, REML = TRUE)
summary(FI_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1M_long
##
## REML criterion at convergence: 2627.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39593 -0.49332 -0.00673 0.48809 2.50102
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 10.482 3.238
## Residual 5.891 2.427
## Number of obs: 489, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.8600 0.5722 351.6690 17.230
## GroupB_Controls 0.1683 0.6942 351.6690 0.242
## GroupC_Intervention 0.6157 0.6974 351.6690 0.883
## TimeD_M1_FI_total -0.1205 0.5119 237.2371 -0.235
## GroupB_Controls:TimeD_M1_FI_total -0.5991 0.6203 237.0764 -0.966
## GroupC_Intervention:TimeD_M1_FI_total -0.9938 0.6226 236.9204 -1.596
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.809
## GroupC_Intervention 0.378
## TimeD_M1_FI_total 0.814
## GroupB_Controls:TimeD_M1_FI_total 0.335
## GroupC_Intervention:TimeD_M1_FI_total 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_FI_t -0.402 0.332 0.330
## GB_C:TD_M1_ 0.332 -0.403 -0.272 -0.825
## GC_I:TD_M1_ 0.331 -0.273 -0.403 -0.822 0.678anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.403 0.702 2 256.73 0.1191 0.887779
## Time 44.086 44.086 1 236.88 7.4834 0.006698 **
## Group:Time 15.160 7.580 2 236.79 1.2867 0.278109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM_B1M)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.86 | 8.74 – 10.98 | <0.001 |
| Group [B_Controls] | 0.17 | -1.20 – 1.53 | 0.809 |
| Group [C_Intervention] | 0.62 | -0.75 – 1.99 | 0.378 |
| Time [D_M1_FI_total] | -0.12 | -1.13 – 0.89 | 0.814 |
|
Group [B_Controls] × Time [D_M1_FI_total] |
-0.60 | -1.82 – 0.62 | 0.335 |
|
Group [C_Intervention] × Time [D_M1_FI_total] |
-0.99 | -2.22 – 0.23 | 0.111 |
| Random Effects | |||
| σ2 | 5.89 | ||
| Ï„00 ID | 10.48 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 489 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.644 | ||
parameters::standardise_parameters(FI_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.01 | [-0.27, 0.29]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.15 | [-0.19, 0.49]
## TimeD_M1_FI_total | -0.03 | [-0.28, 0.22]
## GroupB_Controls:TimeD_M1_FI_total | -0.15 | [-0.45, 0.15]
## GroupC_Intervention:TimeD_M1_FI_total | -0.25 | [-0.55, 0.06]plot_model(FI_MEM_B1M, type = "int")
Extra exploratory: IUS and GM association at PRE
PRE_IUS_GM_lm <- lm(A_PRE_IUS_total ~ A_PRE_GM, data = Full_data_all)
summary(PRE_IUS_GM_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_GM, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.939 -5.868 0.918 6.097 18.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.6537 1.2714 30.402 < 2e-16 ***
## A_PRE_GM 1.2142 0.3875 3.134 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.713 on 257 degrees of freedom
## Multiple R-squared: 0.03681, Adjusted R-squared: 0.03306
## F-statistic: 9.82 on 1 and 257 DF, p-value: 0.001926anova(PRE_IUS_GM_lm) %>%
report()## The ANOVA suggests that:
##
## - The main effect of A_PRE_GM is statistically significant and small (F(1, 257)
## = 9.82, p = 0.002; Eta2 = 0.04, 95% CI [8.34e-03, 1.00])
##
## Effect sizes were labelled following Field's (2013) recommendations.Plot_lm_fi <- ggplot(data = Full_data_all, aes(x = A_PRE_IUS_total, y = A_PRE_GM)) +
geom_point() +
geom_smooth(method = "lm", color = "black")
print(Plot_lm_fi)## `geom_smooth()` using formula = 'y ~ x'