IU Mindset Data Analyses
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library(report)
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library(report)Set up
Reading the full data (intervention group, psychoeducation control group, and EC group) and calculating total scores
Full_data_all_t <- read_csv("MI_Data_B1W1M1.csv") %>%
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_friends, B_FI_strangers, B_FI_work, B_FI_education, B_FI_hobbies, na.rm = TRUE)) %>%
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_1, B_PHQ_2, B_PHQ_3, B_PHQ_4, B_PHQ_5, B_PHQ_6, B_PHQ_7, B_PHQ_8, na.rm = TRUE)) %>%
mutate(A_PRE_GAD_total = sum(B_GAD_1, B_GAD_2, B_GAD_3, B_GAD_4, B_GAD_5, B_GAD_6, B_GAD_7, na.rm = TRUE)) %>%
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_friends, W1_FI_strangers, W1_FI_work, W1_FI_education, W1_FI_hobbies, na.rm = TRUE)) %>%
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_1, W1_PHQ_2, W1_PHQ_3, W1_PHQ_4, W1_PHQ_5, W1_PHQ_6, W1_PHQ_7, W1_PHQ_8, na.rm = TRUE)) %>%
mutate(C_W1_GAD_total = sum(W1_GAD_1, W1_GAD_2, W1_GAD_3, W1_GAD_4, W1_GAD_5, W1_GAD_6, W1_GAD_7, na.rm = TRUE)) %>%
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_friends, M1_FI_strangers, M1_FI_work, M1_FI_education, M1_FI_hobbies, na.rm = TRUE)) %>%
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_1, M1_PHQ_2, M1_PHQ_3, M1_PHQ_4, M1_PHQ_5, M1_PHQ_6, M1_PHQ_7, M1_PHQ_8, na.rm = TRUE)) %>%
mutate(D_M1_GAD_total = sum(M1_GAD_1, M1_GAD_2, M1_GAD_3, M1_GAD_4, M1_GAD_5, M1_GAD_6, M1_GAD_7, na.rm = TRUE)) %>%
ungroup()## New names:
## Rows: 259 Columns: 207
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Prolific_ID, Group dbl (205): ...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`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))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
## -28.943 -4.553 0.081 4.910 22.117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.1756 1.5954 18.914 < 2e-16 ***
## A_PRE_PHQ_total 0.6707 0.0842 7.965 5.36e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.951 on 257 degrees of freedom
## Multiple R-squared: 0.198, Adjusted R-squared: 0.1949
## F-statistic: 63.44 on 1 and 257 DF, p-value: 5.356e-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 4010.8 4010.8 63.444 5.356e-14 ***
## Residuals 257 16246.9 63.2
## ---
## 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
## -25.3319 -4.8319 0.7045 4.1681 20.6317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.26836 1.43778 20.357 <2e-16 ***
## A_PRE_GAD_total 0.82727 0.08645 9.569 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.623 on 257 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2598
## F-statistic: 91.57 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 5321.7 5321.7 91.57 < 2.2e-16 ***
## Residuals 257 14935.9 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: 7807.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4802 -0.3254 0.0609 0.4737 2.3749
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 72.64 8.523
## Residual 77.08 8.779
## Number of obs: 1036, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.7304 600.1493 23.740
## GroupB_Controls 0.9483 2.0993 600.1493 0.452
## GroupC_Intervention 1.9880 2.1090 600.1493 0.943
## TimeB_POST_IUS_total -0.2800 1.7559 768.0000 -0.159
## TimeC_W1_IUS_total -0.7200 1.7559 768.0000 -0.410
## TimeD_M1_IUS_total -2.9000 1.7559 768.0000 -1.652
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 2.1301 768.0000 -1.684
## GroupC_Intervention:TimeB_POST_IUS_total -6.7200 2.1400 768.0000 -3.140
## GroupB_Controls:TimeC_W1_IUS_total -1.8272 2.1301 768.0000 -0.858
## GroupC_Intervention:TimeC_W1_IUS_total -4.6878 2.1400 768.0000 -2.191
## GroupB_Controls:TimeD_M1_IUS_total -3.2415 2.1301 768.0000 -1.522
## GroupC_Intervention:TimeD_M1_IUS_total -6.2553 2.1400 768.0000 -2.923
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.65162
## GroupC_Intervention 0.34627
## TimeB_POST_IUS_total 0.87334
## TimeC_W1_IUS_total 0.68188
## TimeD_M1_IUS_total 0.09902 .
## GroupB_Controls:TimeB_POST_IUS_total 0.09251 .
## GroupC_Intervention:TimeB_POST_IUS_total 0.00175 **
## GroupB_Controls:TimeC_W1_IUS_total 0.39128
## GroupC_Intervention:TimeC_W1_IUS_total 0.02879 *
## GroupB_Controls:TimeD_M1_IUS_total 0.12848
## GroupC_Intervention:TimeD_M1_IUS_total 0.00357 **
## ---
## 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 -0.507 0.418 0.416
## TmC_W1_IUS_ -0.507 0.418 0.416 0.500
## TmD_M1_IUS_ -0.507 0.418 0.416 0.500 0.500
## GB_C:TB_POS 0.418 -0.507 -0.343 -0.824 -0.412 -0.412
## GC_I:TB_POS 0.416 -0.343 -0.507 -0.820 -0.410 -0.410 0.676
## GB_C:TC_W1_ 0.418 -0.507 -0.343 -0.412 -0.824 -0.412 0.500 0.338
## GC_I:TC_W1_ 0.416 -0.343 -0.507 -0.410 -0.820 -0.410 0.338 0.500 0.676
## GB_C:TD_M1_ 0.418 -0.507 -0.343 -0.412 -0.412 -0.824 0.500 0.338 0.500
## GC_I:TD_M1_ 0.416 -0.343 -0.507 -0.410 -0.410 -0.820 0.338 0.500 0.338
## 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_ 0.338
## GC_I:TD_M1_ 0.500 0.676anova (IUS_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 175.6 87.79 2 256 1.1390 0.32176
## Time 4324.7 1441.58 3 768 18.7031 1.022e-11 ***
## Group:Time 1012.0 168.66 6 768 2.1882 0.04221 *
## ---
## 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 | 37.68 – 44.48 | <0.001 |
| Group [B_Controls] | 0.95 | -3.17 – 5.07 | 0.652 |
| Group [C_Intervention] | 1.99 | -2.15 – 6.13 | 0.346 |
| Time [B_POST_IUS_total] | -0.28 | -3.73 – 3.17 | 0.873 |
| Time [C_W1_IUS_total] | -0.72 | -4.17 – 2.73 | 0.682 |
| Time [D_M1_IUS_total] | -2.90 | -6.35 – 0.55 | 0.099 |
|
Group [B_Controls] × Time [B_POST_IUS_total] |
-3.59 | -7.77 – 0.59 | 0.092 |
|
Group [C_Intervention] × Time [B_POST_IUS_total] |
-6.72 | -10.92 – -2.52 | 0.002 |
|
Group [B_Controls] × Time [C_W1_IUS_total] |
-1.83 | -6.01 – 2.35 | 0.391 |
|
Group [C_Intervention] × Time [C_W1_IUS_total] |
-4.69 | -8.89 – -0.49 | 0.029 |
|
Group [B_Controls] × Time [D_M1_IUS_total] |
-3.24 | -7.42 – 0.94 | 0.128 |
|
Group [C_Intervention] × Time [D_M1_IUS_total] |
-6.26 | -10.45 – -2.06 | 0.004 |
| Random Effects | |||
| σ2 | 77.08 | ||
| Ï„00 ID | 72.64 | ||
| ICC | 0.49 | ||
| N ID | 259 | ||
| Observations | 1036 | ||
| Marginal R2 / Conditional R2 | 0.049 / 0.510 | ||
parameters::standardise_parameters(IUS_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.20 | [-0.08, 0.47]
## GroupB_Controls | 0.08 | [-0.25, 0.41]
## GroupC_Intervention | 0.16 | [-0.17, 0.49]
## TimeB_POST_IUS_total | -0.02 | [-0.30, 0.25]
## TimeC_W1_IUS_total | -0.06 | [-0.33, 0.22]
## TimeD_M1_IUS_total | -0.23 | [-0.51, 0.04]
## GroupB_Controls:TimeB_POST_IUS_total | -0.29 | [-0.62, 0.05]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.54 | [-0.87, -0.20]
## GroupB_Controls:TimeC_W1_IUS_total | -0.15 | [-0.48, 0.19]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.38 | [-0.71, -0.04]
## GroupB_Controls:TimeD_M1_IUS_total | -0.26 | [-0.59, 0.08]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.50 | [-0.84, -0.16]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 = 0.51) and the part related
## to the fixed effects alone (marginal R2) is of 0.05. The model's intercept,
## corresponding to Group = A_ECs and Time = A_PRE_IUS_total, is at 41.08 (95% CI
## [37.68, 44.48], t(1022) = 23.74, p < .001). Within this model:
##
## - The effect of Group [B_Controls] is statistically non-significant and
## positive (beta = 0.95, 95% CI [-3.17, 5.07], t(1022) = 0.45, p = 0.652; Std.
## beta = 0.08, 95% CI [-0.25, 0.41])
## - The effect of Group [C_Intervention] is statistically non-significant and
## positive (beta = 1.99, 95% CI [-2.15, 6.13], t(1022) = 0.94, p = 0.346; Std.
## beta = 0.16, 95% CI [-0.17, 0.49])
## - The effect of Time [B_POST_IUS_total] is statistically non-significant and
## negative (beta = -0.28, 95% CI [-3.73, 3.17], t(1022) = -0.16, p = 0.873; Std.
## beta = -0.02, 95% CI [-0.30, 0.25])
## - The effect of Time [C_W1_IUS_total] is statistically non-significant and
## negative (beta = -0.72, 95% CI [-4.17, 2.73], t(1022) = -0.41, p = 0.682; Std.
## beta = -0.06, 95% CI [-0.33, 0.22])
## - The effect of Time [D_M1_IUS_total] is statistically non-significant and
## negative (beta = -2.90, 95% CI [-6.35, 0.55], t(1022) = -1.65, p = 0.099; Std.
## beta = -0.23, 95% CI [-0.51, 0.04])
## - The effect of Group [B_Controls] × Time [B_POST_IUS_total] is statistically
## non-significant and negative (beta = -3.59, 95% CI [-7.77, 0.59], t(1022) =
## -1.68, p = 0.092; Std. beta = -0.29, 95% CI [-0.62, 0.05])
## - The effect of Group [C_Intervention] × Time [B_POST_IUS_total] is
## statistically significant and negative (beta = -6.72, 95% CI [-10.92, -2.52],
## t(1022) = -3.14, p = 0.002; Std. beta = -0.54, 95% CI [-0.87, -0.20])
## - The effect of Group [B_Controls] × Time [C_W1_IUS_total] is statistically
## non-significant and negative (beta = -1.83, 95% CI [-6.01, 2.35], t(1022) =
## -0.86, p = 0.391; Std. beta = -0.15, 95% CI [-0.48, 0.19])
## - The effect of Group [C_Intervention] × Time [C_W1_IUS_total] is statistically
## significant and negative (beta = -4.69, 95% CI [-8.89, -0.49], t(1022) = -2.19,
## p = 0.029; Std. beta = -0.38, 95% CI [-0.71, -0.04])
## - The effect of Group [B_Controls] × Time [D_M1_IUS_total] is statistically
## non-significant and negative (beta = -3.24, 95% CI [-7.42, 0.94], t(1022) =
## -1.52, p = 0.128; Std. beta = -0.26, 95% CI [-0.59, 0.08])
## - The effect of Group [C_Intervention] × Time [D_M1_IUS_total] is statistically
## significant and negative (beta = -6.26, 95% CI [-10.45, -2.06], t(1022) =
## -2.92, p = 0.004; Std. beta = -0.50, 95% CI [-0.84, -0.16])
##
## 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.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: 3656.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2320 -0.4094 0.0034 0.4135 3.2300
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 71.59 8.461
## Residual 28.73 5.360
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.4165 339.2570 29.002
## GroupB_Controls 0.9483 1.7184 339.2570 0.552
## GroupC_Intervention 1.9880 1.7264 339.2570 1.152
## TimeB_POST_IUS_total -0.2800 1.0721 256.0000 -0.261
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 1.3006 256.0000 -2.759
## GroupC_Intervention:TimeB_POST_IUS_total -6.7200 1.3067 256.0000 -5.143
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.58141
## GroupC_Intervention 0.25033
## TimeB_POST_IUS_total 0.79417
## GroupB_Controls:TimeB_POST_IUS_total 0.00622 **
## GroupC_Intervention:TimeB_POST_IUS_total 5.38e-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 -0.378 0.312 0.311
## GB_C:TB_POS 0.312 -0.378 -0.256 -0.824
## GC_I:TB_POS 0.311 -0.256 -0.378 -0.820 0.676anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 21.31 10.66 2 256 0.3708 0.6905
## Time 1587.48 1587.48 1 256 55.2457 1.595e-12 ***
## Group:Time 787.45 393.72 2 256 13.7020 2.222e-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 | 38.30 – 43.86 | <0.001 |
| Group [B_Controls] | 0.95 | -2.43 – 4.32 | 0.581 |
| Group [C_Intervention] | 1.99 | -1.40 – 5.38 | 0.250 |
| Time [B_POST_IUS_total] | -0.28 | -2.39 – 1.83 | 0.794 |
|
Group [B_Controls] × Time [B_POST_IUS_total] |
-3.59 | -6.14 – -1.03 | 0.006 |
|
Group [C_Intervention] × Time [B_POST_IUS_total] |
-6.72 | -9.29 – -4.15 | <0.001 |
| Random Effects | |||
| σ2 | 28.73 | ||
| Ï„00 ID | 71.59 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.062 / 0.731 | ||
parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.37]
## GroupB_Controls | 0.09 | [-0.24, 0.42]
## GroupC_Intervention | 0.19 | [-0.14, 0.52]
## TimeB_POST_IUS_total | -0.03 | [-0.23, 0.18]
## GroupB_Controls:TimeB_POST_IUS_total | -0.35 | [-0.60, -0.10]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.65 | [-0.90, -0.40]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: 3746.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9845 -0.3439 0.0235 0.4237 2.5991
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 69.60 8.342
## Residual 39.31 6.270
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.4759 363.5479 27.834
## GroupB_Controls 0.9483 1.7904 363.5479 0.530
## GroupC_Intervention 1.9880 1.7988 363.5479 1.105
## TimeC_W1_IUS_total -0.7200 1.2540 256.0000 -0.574
## GroupB_Controls:TimeC_W1_IUS_total -1.8272 1.5213 256.0000 -1.201
## GroupC_Intervention:TimeC_W1_IUS_total -4.6878 1.5284 256.0000 -3.067
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.59668
## GroupC_Intervention 0.26981
## TimeC_W1_IUS_total 0.56637
## GroupB_Controls:TimeC_W1_IUS_total 0.23084
## GroupC_Intervention:TimeC_W1_IUS_total 0.00239 **
## ---
## 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_ -0.425 0.350 0.349
## GB_C:TC_W1_ 0.350 -0.425 -0.287 -0.824
## GC_I:TC_W1_ 0.349 -0.287 -0.425 -0.820 0.676anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 3.95 1.98 2 256 0.0503 0.950967
## Time 961.28 961.28 1 256 24.4509 1.381e-06 ***
## Group:Time 425.11 212.56 2 256 5.4065 0.005014 **
## ---
## 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 | 38.18 – 43.98 | <0.001 |
| Group [B_Controls] | 0.95 | -2.57 – 4.47 | 0.597 |
| Group [C_Intervention] | 1.99 | -1.55 – 5.52 | 0.270 |
| Time [C_W1_IUS_total] | -0.72 | -3.18 – 1.74 | 0.566 |
|
Group [B_Controls] × Time [C_W1_IUS_total] |
-1.83 | -4.82 – 1.16 | 0.230 |
|
Group [C_Intervention] × Time [C_W1_IUS_total] |
-4.69 | -7.69 – -1.69 | 0.002 |
| Random Effects | |||
| σ2 | 39.31 | ||
| Ï„00 ID | 69.60 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.651 | ||
parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.05 | [-0.23, 0.32]
## GroupB_Controls | 0.09 | [-0.24, 0.42]
## GroupC_Intervention | 0.19 | [-0.15, 0.52]
## TimeC_W1_IUS_total | -0.07 | [-0.30, 0.17]
## GroupB_Controls:TimeC_W1_IUS_total | -0.17 | [-0.46, 0.11]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.44 | [-0.73, -0.16]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: 4070.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92417 -0.36721 0.08337 0.54180 1.88810
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.84 7.735
## Residual 108.95 10.438
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.8373 454.8351 22.359
## GroupB_Controls 0.9483 2.2289 454.8351 0.425
## GroupC_Intervention 1.9880 2.2393 454.8351 0.888
## TimeD_M1_IUS_total -2.9000 2.0876 256.0000 -1.389
## GroupB_Controls:TimeD_M1_IUS_total -3.2415 2.5325 256.0000 -1.280
## GroupC_Intervention:TimeD_M1_IUS_total -6.2553 2.5443 256.0000 -2.459
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6707
## GroupC_Intervention 0.3751
## TimeD_M1_IUS_total 0.1660
## GroupB_Controls:TimeD_M1_IUS_total 0.2017
## GroupC_Intervention:TimeD_M1_IUS_total 0.0146 *
## ---
## 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_ -0.568 0.468 0.466
## GB_C:TD_M1_ 0.468 -0.568 -0.384 -0.824
## GC_I:TD_M1_ 0.466 -0.384 -0.568 -0.820 0.676anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 42.2 21.1 2 256 0.1937 0.8240
## Time 4229.7 4229.7 1 256 38.8236 1.896e-09 ***
## Group:Time 688.0 344.0 2 256 3.1574 0.0442 *
## ---
## 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 | 37.47 – 44.69 | <0.001 |
| Group [B_Controls] | 0.95 | -3.43 – 5.33 | 0.671 |
| Group [C_Intervention] | 1.99 | -2.41 – 6.39 | 0.375 |
| Time [D_M1_IUS_total] | -2.90 | -7.00 – 1.20 | 0.165 |
|
Group [B_Controls] × Time [D_M1_IUS_total] |
-3.24 | -8.22 – 1.73 | 0.201 |
|
Group [C_Intervention] × Time [D_M1_IUS_total] |
-6.26 | -11.25 – -1.26 | 0.014 |
| Random Effects | |||
| σ2 | 108.95 | ||
| Ï„00 ID | 59.84 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.070 / 0.400 | ||
parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.16 | [-0.11, 0.43]
## GroupB_Controls | 0.07 | [-0.26, 0.40]
## GroupC_Intervention | 0.15 | [-0.18, 0.48]
## TimeD_M1_IUS_total | -0.22 | [-0.52, 0.09]
## GroupB_Controls:TimeD_M1_IUS_total | -0.24 | [-0.61, 0.13]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.47 | [-0.84, -0.09]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]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: 5050.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2687 -0.4532 -0.0281 0.4903 2.4898
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.66 4.545
## Residual 26.75 5.172
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.9600 0.9737 556.5908 18.445
## GroupB_Controls -0.5355 1.1812 556.5908 -0.453
## GroupC_Intervention 0.6905 1.1867 556.5908 0.582
## TimeC_W1_PHQ_total -0.7600 1.0343 512.0000 -0.735
## TimeD_M1_PHQ_total -2.1800 1.0343 512.0000 -2.108
## GroupB_Controls:TimeC_W1_PHQ_total -0.5702 1.2548 512.0000 -0.454
## GroupC_Intervention:TimeC_W1_PHQ_total -1.2206 1.2606 512.0000 -0.968
## GroupB_Controls:TimeD_M1_PHQ_total -0.9426 1.2548 512.0000 -0.751
## GroupC_Intervention:TimeD_M1_PHQ_total -2.0336 1.2606 512.0000 -1.613
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6505
## GroupC_Intervention 0.5609
## TimeC_W1_PHQ_total 0.4628
## TimeD_M1_PHQ_total 0.0355 *
## GroupB_Controls:TimeC_W1_PHQ_total 0.6497
## GroupC_Intervention:TimeC_W1_PHQ_total 0.3334
## GroupB_Controls:TimeD_M1_PHQ_total 0.4529
## GroupC_Intervention:TimeD_M1_PHQ_total 0.1073
## ---
## 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_ -0.531 0.438 0.436
## TmD_M1_PHQ_ -0.531 0.438 0.436 0.500
## GB_C:TC_W1_ 0.438 -0.531 -0.359 -0.824 -0.412
## GC_I:TC_W1_ 0.436 -0.359 -0.531 -0.820 -0.410 0.676
## GB_C:TD_M1_ 0.438 -0.531 -0.359 -0.412 -0.824 0.500 0.338
## GC_I:TD_M1_ 0.436 -0.359 -0.531 -0.410 -0.820 0.338 0.500 0.676anova (PHQ_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 38.69 19.34 2 256 0.7232 0.4862
## Time 1164.82 582.41 2 512 21.7752 8.392e-10 ***
## Group:Time 76.29 19.07 4 512 0.7130 0.5833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM)| Â | PHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 17.96 | 16.05 – 19.87 | <0.001 |
| Group [B_Controls] | -0.54 | -2.85 – 1.78 | 0.650 |
| Group [C_Intervention] | 0.69 | -1.64 – 3.02 | 0.561 |
| Time [C_W1_PHQ_total] | -0.76 | -2.79 – 1.27 | 0.463 |
| Time [D_M1_PHQ_total] | -2.18 | -4.21 – -0.15 | 0.035 |
|
Group [B_Controls] × Time [C_W1_PHQ_total] |
-0.57 | -3.03 – 1.89 | 0.650 |
|
Group [C_Intervention] × Time [C_W1_PHQ_total] |
-1.22 | -3.70 – 1.25 | 0.333 |
|
Group [B_Controls] × Time [D_M1_PHQ_total] |
-0.94 | -3.41 – 1.52 | 0.453 |
|
Group [C_Intervention] × Time [D_M1_PHQ_total] |
-2.03 | -4.51 – 0.44 | 0.107 |
| Random Effects | |||
| σ2 | 26.75 | ||
| Ï„00 ID | 20.66 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.044 / 0.461 | ||
parameters::standardise_parameters(PHQ_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.22 | [-0.05, 0.50]
## GroupB_Controls | -0.08 | [-0.41, 0.25]
## GroupC_Intervention | 0.10 | [-0.23, 0.43]
## TimeC_W1_PHQ_total | -0.11 | [-0.40, 0.18]
## TimeD_M1_PHQ_total | -0.31 | [-0.60, -0.02]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.08 | [-0.43, 0.27]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.17 | [-0.53, 0.18]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.13 | [-0.49, 0.22]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.29 | [-0.64, 0.06]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: 3213.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09297 -0.46311 -0.00509 0.43990 2.68855
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 25.22 5.022
## Residual 13.65 3.695
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.9600 0.8817 360.3455 20.370
## GroupB_Controls -0.5355 1.0696 360.3455 -0.501
## GroupC_Intervention 0.6905 1.0746 360.3455 0.643
## TimeC_W1_PHQ_total -0.7600 0.7390 256.0000 -1.028
## GroupB_Controls:TimeC_W1_PHQ_total -0.5702 0.8965 256.0000 -0.636
## GroupC_Intervention:TimeC_W1_PHQ_total -1.2206 0.9007 256.0000 -1.355
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.617
## GroupC_Intervention 0.521
## TimeC_W1_PHQ_total 0.305
## GroupB_Controls:TimeC_W1_PHQ_total 0.525
## GroupC_Intervention:TimeC_W1_PHQ_total 0.177
## ---
## 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_ -0.419 0.345 0.344
## GB_C:TC_W1_ 0.345 -0.419 -0.283 -0.824
## GC_I:TC_W1_ 0.344 -0.283 -0.419 -0.820 0.676anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 20.499 10.250 2 256 0.7507 0.4730687
## Time 211.676 211.676 1 256 15.5036 0.0001062 ***
## Group:Time 27.054 13.527 2 256 0.9908 0.3727148
## ---
## 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) | 17.96 | 16.23 – 19.69 | <0.001 |
| Group [B_Controls] | -0.54 | -2.64 – 1.57 | 0.617 |
| Group [C_Intervention] | 0.69 | -1.42 – 2.80 | 0.521 |
| Time [C_W1_PHQ_total] | -0.76 | -2.21 – 0.69 | 0.304 |
|
Group [B_Controls] × Time [C_W1_PHQ_total] |
-0.57 | -2.33 – 1.19 | 0.525 |
|
Group [C_Intervention] × Time [C_W1_PHQ_total] |
-1.22 | -2.99 – 0.55 | 0.176 |
| Random Effects | |||
| σ2 | 13.65 | ||
| Ï„00 ID | 25.22 | ||
| ICC | 0.65 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.656 | ||
parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.11 | [-0.17, 0.39]
## GroupB_Controls | -0.09 | [-0.42, 0.25]
## GroupC_Intervention | 0.11 | [-0.23, 0.45]
## TimeC_W1_PHQ_total | -0.12 | [-0.35, 0.11]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.09 | [-0.37, 0.19]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.19 | [-0.48, 0.09]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: 3443.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38575 -0.50846 -0.02286 0.56387 2.22387
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 17.49 4.182
## Residual 32.01 5.658
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.9600 0.9950 455.1887 18.051
## GroupB_Controls -0.5355 1.2070 455.1887 -0.444
## GroupC_Intervention 0.6905 1.2127 455.1887 0.569
## TimeD_M1_PHQ_total -2.1800 1.1316 256.0000 -1.927
## GroupB_Controls:TimeD_M1_PHQ_total -0.9426 1.3728 256.0000 -0.687
## GroupC_Intervention:TimeD_M1_PHQ_total -2.0336 1.3792 256.0000 -1.475
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6575
## GroupC_Intervention 0.5694
## TimeD_M1_PHQ_total 0.0551 .
## GroupB_Controls:TimeD_M1_PHQ_total 0.4929
## GroupC_Intervention:TimeD_M1_PHQ_total 0.1416
## ---
## 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_ -0.569 0.469 0.467
## GB_C:TD_M1_ 0.469 -0.569 -0.385 -0.824
## GC_I:TD_M1_ 0.467 -0.385 -0.569 -0.820 0.676anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 40.51 20.25 2 256 0.6327 0.5320
## Time 1156.78 1156.78 1 256 36.1358 6.308e-09 ***
## Group:Time 75.29 37.65 2 256 1.1760 0.3102
## ---
## 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) | 17.96 | 16.01 – 19.91 | <0.001 |
| Group [B_Controls] | -0.54 | -2.91 – 1.84 | 0.658 |
| Group [C_Intervention] | 0.69 | -1.69 – 3.07 | 0.569 |
| Time [D_M1_PHQ_total] | -2.18 | -4.40 – 0.04 | 0.055 |
|
Group [B_Controls] × Time [D_M1_PHQ_total] |
-0.94 | -3.64 – 1.75 | 0.493 |
|
Group [C_Intervention] × Time [D_M1_PHQ_total] |
-2.03 | -4.74 – 0.68 | 0.141 |
| Random Effects | |||
| σ2 | 32.01 | ||
| Ï„00 ID | 17.49 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.060 / 0.392 | ||
parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.23 | [-0.04, 0.50]
## GroupB_Controls | -0.07 | [-0.40, 0.25]
## GroupC_Intervention | 0.10 | [-0.23, 0.43]
## TimeD_M1_PHQ_total | -0.30 | [-0.61, 0.01]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.13 | [-0.50, 0.24]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.28 | [-0.66, 0.09]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: 4917.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4172 -0.4747 -0.0001 0.5239 3.2139
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.27 4.389
## Residual 21.80 4.669
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.0200 0.9063 533.2553 16.573
## GroupB_Controls 0.4517 1.0994 533.2553 0.411
## GroupC_Intervention 1.2518 1.1045 533.2553 1.133
## TimeC_W1_GAD_total -0.2600 0.9338 512.0000 -0.278
## TimeD_M1_GAD_total -1.1400 0.9338 512.0000 -1.221
## GroupB_Controls:TimeC_W1_GAD_total -0.7494 1.1328 512.0000 -0.662
## GroupC_Intervention:TimeC_W1_GAD_total -1.2254 1.1381 512.0000 -1.077
## GroupB_Controls:TimeD_M1_GAD_total -1.6053 1.1328 512.0000 -1.417
## GroupC_Intervention:TimeD_M1_GAD_total -2.2386 1.1381 512.0000 -1.967
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6813
## GroupC_Intervention 0.2576
## TimeC_W1_GAD_total 0.7808
## TimeD_M1_GAD_total 0.2227
## GroupB_Controls:TimeC_W1_GAD_total 0.5086
## GroupC_Intervention:TimeC_W1_GAD_total 0.2821
## GroupB_Controls:TimeD_M1_GAD_total 0.1571
## GroupC_Intervention:TimeD_M1_GAD_total 0.0497 *
## ---
## 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_ -0.515 0.425 0.423
## TmD_M1_GAD_ -0.515 0.425 0.423 0.500
## GB_C:TC_W1_ 0.425 -0.515 -0.348 -0.824 -0.412
## GC_I:TC_W1_ 0.423 -0.348 -0.515 -0.820 -0.410 0.676
## GB_C:TD_M1_ 0.425 -0.515 -0.348 -0.412 -0.824 0.500 0.338
## GC_I:TD_M1_ 0.423 -0.348 -0.515 -0.410 -0.820 0.338 0.500 0.676anova (GAD_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 8.44 4.22 2 256 0.1935 0.8242
## Time 687.11 343.55 2 512 15.7595 2.281e-07 ***
## Group:Time 85.55 21.39 4 512 0.9811 0.4174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.02 | 13.24 – 16.80 | <0.001 |
| Group [B_Controls] | 0.45 | -1.71 – 2.61 | 0.681 |
| Group [C_Intervention] | 1.25 | -0.92 – 3.42 | 0.257 |
| Time [C_W1_GAD_total] | -0.26 | -2.09 – 1.57 | 0.781 |
| Time [D_M1_GAD_total] | -1.14 | -2.97 – 0.69 | 0.223 |
|
Group [B_Controls] × Time [C_W1_GAD_total] |
-0.75 | -2.97 – 1.47 | 0.508 |
|
Group [C_Intervention] × Time [C_W1_GAD_total] |
-1.23 | -3.46 – 1.01 | 0.282 |
|
Group [B_Controls] × Time [D_M1_GAD_total] |
-1.61 | -3.83 – 0.62 | 0.157 |
|
Group [C_Intervention] × Time [D_M1_GAD_total] |
-2.24 | -4.47 – -0.00 | 0.050 |
| Random Effects | |||
| σ2 | 21.80 | ||
| Ï„00 ID | 19.27 | ||
| ICC | 0.47 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.486 | ||
parameters::standardise_parameters(GAD_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.19, 0.36]
## GroupB_Controls | 0.07 | [-0.26, 0.40]
## GroupC_Intervention | 0.19 | [-0.14, 0.53]
## TimeC_W1_GAD_total | -0.04 | [-0.32, 0.24]
## TimeD_M1_GAD_total | -0.18 | [-0.46, 0.11]
## GroupB_Controls:TimeC_W1_GAD_total | -0.12 | [-0.46, 0.23]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.19 | [-0.53, 0.16]
## GroupB_Controls:TimeD_M1_GAD_total | -0.25 | [-0.59, 0.10]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.35 | [-0.69, 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: 3156.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.97971 -0.46162 -0.05959 0.48166 2.72962
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.31 4.723
## Residual 12.31 3.508
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.0200 0.8320 361.7397 18.052
## GroupB_Controls 0.4517 1.0094 361.7397 0.448
## GroupC_Intervention 1.2518 1.0141 361.7397 1.234
## TimeC_W1_GAD_total -0.2600 0.7016 256.0000 -0.371
## GroupB_Controls:TimeC_W1_GAD_total -0.7494 0.8511 256.0000 -0.881
## GroupC_Intervention:TimeC_W1_GAD_total -1.2254 0.8551 256.0000 -1.433
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.655
## GroupC_Intervention 0.218
## TimeC_W1_GAD_total 0.711
## GroupB_Controls:TimeC_W1_GAD_total 0.379
## GroupC_Intervention:TimeC_W1_GAD_total 0.153
## ---
## 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_ -0.422 0.348 0.346
## GB_C:TC_W1_ 0.348 -0.422 -0.285 -0.824
## GC_I:TC_W1_ 0.346 -0.285 -0.422 -0.820 0.676anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 9.323 4.662 2 256 0.3788 0.685040
## Time 96.944 96.944 1 256 7.8782 0.005388 **
## Group:Time 25.452 12.726 2 256 1.0342 0.356993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_B1W)| Â | GAD Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.02 | 13.39 – 16.65 | <0.001 |
| Group [B_Controls] | 0.45 | -1.53 – 2.43 | 0.655 |
| Group [C_Intervention] | 1.25 | -0.74 – 3.24 | 0.218 |
| Time [C_W1_GAD_total] | -0.26 | -1.64 – 1.12 | 0.711 |
|
Group [B_Controls] × Time [C_W1_GAD_total] |
-0.75 | -2.42 – 0.92 | 0.379 |
|
Group [C_Intervention] × Time [C_W1_GAD_total] |
-1.23 | -2.91 – 0.45 | 0.152 |
| Random Effects | |||
| σ2 | 12.31 | ||
| Ï„00 ID | 22.31 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.649 | ||
parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.08 | [-0.26, 0.41]
## GroupC_Intervention | 0.21 | [-0.13, 0.55]
## TimeC_W1_GAD_total | -0.04 | [-0.28, 0.19]
## GroupB_Controls:TimeC_W1_GAD_total | -0.13 | [-0.41, 0.16]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.21 | [-0.49, 0.08]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: 3351.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.49775 -0.51016 -0.03334 0.55438 2.48053
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 16.32 4.039
## Residual 25.74 5.073
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 15.0200 0.9171 445.0075 16.378
## GroupB_Controls 0.4517 1.1125 445.0075 0.406
## GroupC_Intervention 1.2518 1.1177 445.0075 1.120
## TimeD_M1_GAD_total -1.1400 1.0146 256.0000 -1.124
## GroupB_Controls:TimeD_M1_GAD_total -1.6053 1.2308 256.0000 -1.304
## GroupC_Intervention:TimeD_M1_GAD_total -2.2386 1.2366 256.0000 -1.810
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6849
## GroupC_Intervention 0.2633
## TimeD_M1_GAD_total 0.2622
## GroupB_Controls:TimeD_M1_GAD_total 0.1933
## GroupC_Intervention:TimeD_M1_GAD_total 0.0714 .
## ---
## 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_ -0.553 0.456 0.454
## GB_C:TD_M1_ 0.456 -0.553 -0.374 -0.824
## GC_I:TD_M1_ 0.454 -0.374 -0.553 -0.820 0.676anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 11.22 5.61 2 256 0.2180 0.8043
## Time 674.00 674.00 1 256 26.1900 6.079e-07 ***
## Group:Time 84.65 42.32 2 256 1.6446 0.1951
## ---
## 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) | 15.02 | 13.22 – 16.82 | <0.001 |
| Group [B_Controls] | 0.45 | -1.73 – 2.64 | 0.685 |
| Group [C_Intervention] | 1.25 | -0.94 – 3.45 | 0.263 |
| Time [D_M1_GAD_total] | -1.14 | -3.13 – 0.85 | 0.262 |
|
Group [B_Controls] × Time [D_M1_GAD_total] |
-1.61 | -4.02 – 0.81 | 0.193 |
|
Group [C_Intervention] × Time [D_M1_GAD_total] |
-2.24 | -4.67 – 0.19 | 0.071 |
| Random Effects | |||
| σ2 | 25.74 | ||
| Ï„00 ID | 16.32 | ||
| ICC | 0.39 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.046 / 0.416 | ||
parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.37]
## GroupB_Controls | 0.07 | [-0.26, 0.40]
## GroupC_Intervention | 0.19 | [-0.14, 0.52]
## TimeD_M1_GAD_total | -0.17 | [-0.47, 0.13]
## GroupB_Controls:TimeD_M1_GAD_total | -0.24 | [-0.61, 0.12]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.34 | [-0.71, 0.03]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")H3: 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 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## PHQ_B1W_change ~
## Group (c1) -0.008 0.076 -0.110 0.912 -0.008 -0.006
## IUS_B1W_change ~
## Group (a1) -0.269 0.085 -3.152 0.002 -0.269 -0.199
## PHQ_B1W_change ~
## IUS_B1W_c (b1) 0.408 0.085 4.780 0.000 0.408 0.408
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.018 0.166 0.111 0.912 0.018 0.019
## .IUS_B1W_change 0.592 0.196 3.018 0.003 0.592 0.593
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.829 0.092 8.992 0.000 0.829 0.833
## .IUS_B1W_change 0.957 0.158 6.071 0.000 0.957 0.960
##
## R-Square:
## Estimate
## PHQ_B1W_change 0.167
## IUS_B1W_change 0.040
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.110 0.039 -2.790 0.005 -0.110 -0.081
## direct -0.008 0.076 -0.110 0.912 -0.008 -0.006
## total -0.118 0.084 -1.408 0.159 -0.118 -0.088Intervention 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 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## 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
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.097 0.014 6.784 0.000 0.097 0.549
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.008 0.015 0.534 0.593 0.008 0.047
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.278 0.123 2.254 0.024 0.278 0.278
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.815 0.247 -7.340 0.000 -1.815 -1.823
## .IUS_B1W_change -0.154 0.311 -0.495 0.621 -0.154 -0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.601 0.093 6.493 0.000 0.601 0.607
## .IUS_B1W_change 0.988 0.223 4.429 0.000 0.988 0.998
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.393
## IUS_B1W_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.002 0.004 0.555 0.579 0.002 0.013
## direct 0.097 0.014 6.784 0.000 0.097 0.549
## total 0.100 0.015 6.827 0.000 0.100 0.562Psychoeducation 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 31 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
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.118 0.011 10.736 0.000 0.118 0.722
## IUS_B1W_change ~
## A_PRE_PHQ (a1) -0.007 0.013 -0.558 0.577 -0.007 -0.043
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.302 0.080 3.785 0.000 0.302 0.302
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -2.048 0.178 -11.515 0.000 -2.048 -2.058
## .IUS_B1W_change 0.123 0.243 0.507 0.612 0.123 0.124
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.402 0.062 6.491 0.000 0.402 0.406
## .IUS_B1W_change 0.989 0.233 4.249 0.000 0.989 0.998
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.594
## IUS_B1W_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.002 0.004 -0.563 0.573 -0.002 -0.013
## direct 0.118 0.011 10.736 0.000 0.118 0.722
## total 0.115 0.011 10.108 0.000 0.115 0.709EC 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 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
## C_W1_PHQ_total ~
## A_PRE_PHQ (c1) 0.104 0.008 12.929 0.000 0.104 0.602
## IUS_B1W_change ~
## A_PRE_PHQ (a1) 0.036 0.013 2.654 0.008 0.036 0.206
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.503 0.051 9.905 0.000 0.503 0.503
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.876 0.150 -12.504 0.000 -1.876 -1.895
## .IUS_B1W_change -0.643 0.303 -2.118 0.034 -0.643 -0.649
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.254 0.073 3.473 0.001 0.254 0.259
## .IUS_B1W_change 0.938 0.464 2.024 0.043 0.938 0.957
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.741
## IUS_B1W_change 0.043
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.018 0.007 2.505 0.012 0.018 0.104
## direct 0.104 0.008 12.929 0.000 0.104 0.602
## total 0.122 0.009 13.339 0.000 0.122 0.706Full 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 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## PHQ_B1M_change ~
## Group (c1) 0.008 0.063 0.130 0.897 0.008 0.006
## IUS_B1M_change ~
## Group (a1) -0.209 0.083 -2.526 0.012 -0.209 -0.155
## PHQ_B1M_change ~
## IUS_B1M_c (b1) 0.653 0.052 12.491 0.000 0.653 0.653
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change -0.018 0.147 -0.122 0.903 -0.018 -0.018
## .IUS_B1M_change 0.461 0.192 2.396 0.017 0.461 0.462
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.572 0.064 9.005 0.000 0.572 0.574
## .IUS_B1M_change 0.972 0.122 7.988 0.000 0.972 0.976
##
## R-Square:
## Estimate
## PHQ_B1M_change 0.426
## IUS_B1M_change 0.024
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.137 0.054 -2.547 0.011 -0.137 -0.101
## direct 0.008 0.063 0.130 0.897 0.008 0.006
## total -0.128 0.084 -1.526 0.127 -0.128 -0.095Intervention 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 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## 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
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.082 0.010 8.324 0.000 0.082 0.464
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.005 0.015 -0.334 0.738 -0.005 -0.028
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.565 0.081 6.974 0.000 0.565 0.565
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.535 0.186 -8.247 0.000 -1.535 -1.543
## .IUS_B1M_change 0.093 0.290 0.322 0.748 0.093 0.094
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.476 0.075 6.303 0.000 0.476 0.481
## .IUS_B1M_change 0.990 0.184 5.381 0.000 0.990 0.999
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.519
## IUS_B1M_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.009 -0.330 0.741 -0.003 -0.016
## direct 0.082 0.010 8.324 0.000 0.082 0.464
## total 0.079 0.014 5.820 0.000 0.079 0.448Psychoeducation 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 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
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.066 0.011 5.895 0.000 0.066 0.403
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.023 0.017 -1.375 0.169 -0.023 -0.142
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.615 0.070 8.805 0.000 0.615 0.615
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.143 0.198 -5.783 0.000 -1.143 -1.149
## .IUS_B1M_change 0.403 0.273 1.474 0.140 0.403 0.404
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.524 0.079 6.655 0.000 0.524 0.529
## .IUS_B1M_change 0.971 0.183 5.306 0.000 0.971 0.980
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.471
## IUS_B1M_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.014 0.011 -1.295 0.195 -0.014 -0.087
## direct 0.066 0.011 5.895 0.000 0.066 0.403
## total 0.051 0.015 3.474 0.001 0.051 0.316EC 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 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
## D_M1_PHQ_total ~
## A_PRE_PHQ (c1) 0.067 0.016 4.242 0.000 0.067 0.386
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.009 0.023 -0.419 0.676 -0.009 -0.055
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.727 0.058 12.599 0.000 0.727 0.727
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.202 0.265 -4.537 0.000 -1.202 -1.214
## .IUS_B1M_change 0.170 0.382 0.445 0.656 0.170 0.172
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.346 0.120 2.882 0.004 0.346 0.353
## .IUS_B1M_change 0.977 0.297 3.293 0.001 0.977 0.997
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.647
## IUS_B1M_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.007 0.017 -0.414 0.679 -0.007 -0.040
## direct 0.067 0.016 4.242 0.000 0.067 0.386
## total 0.060 0.024 2.534 0.011 0.060 0.346H3: 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 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## GAD_B1W_change ~
## Group (c1) 0.002 0.072 0.023 0.982 0.002 0.001
## IUS_B1W_change ~
## Group (a1) -0.269 0.085 -3.152 0.002 -0.269 -0.199
## GAD_B1W_change ~
## IUS_B1W_c (b1) 0.450 0.081 5.573 0.000 0.450 0.450
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change -0.004 0.151 -0.024 0.981 -0.004 -0.004
## .IUS_B1W_change 0.592 0.196 3.018 0.003 0.592 0.593
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.795 0.107 7.396 0.000 0.795 0.798
## .IUS_B1W_change 0.957 0.158 6.071 0.000 0.957 0.960
##
## R-Square:
## Estimate
## GAD_B1W_change 0.202
## IUS_B1W_change 0.040
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.121 0.045 -2.715 0.007 -0.121 -0.090
## direct 0.002 0.072 0.023 0.982 0.002 0.001
## total -0.119 0.081 -1.471 0.141 -0.119 -0.089Intervention 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 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## 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
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.108 0.014 7.546 0.000 0.108 0.589
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.004 0.017 -0.233 0.815 -0.004 -0.022
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.286 0.121 2.372 0.018 0.286 0.286
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.750 0.208 -8.401 0.000 -1.750 -1.759
## .IUS_B1W_change 0.065 0.299 0.217 0.828 0.065 0.065
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.573 0.106 5.421 0.000 0.573 0.578
## .IUS_B1W_change 0.990 0.223 4.434 0.000 0.990 1.000
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.422
## IUS_B1W_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.005 -0.224 0.823 -0.001 -0.006
## direct 0.108 0.014 7.546 0.000 0.108 0.589
## total 0.106 0.014 7.366 0.000 0.106 0.583Psychoeducation 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 29 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
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.122 0.010 12.186 0.000 0.122 0.706
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.009 0.013 -0.694 0.488 -0.009 -0.053
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.343 0.065 5.253 0.000 0.343 0.343
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.891 0.163 -11.636 0.000 -1.891 -1.900
## .IUS_B1W_change 0.142 0.231 0.614 0.539 0.142 0.143
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.407 0.075 5.397 0.000 0.407 0.410
## .IUS_B1W_change 0.988 0.232 4.253 0.000 0.988 0.997
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.590
## IUS_B1W_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.004 -0.703 0.482 -0.003 -0.018
## direct 0.122 0.010 12.186 0.000 0.122 0.706
## total 0.119 0.011 11.113 0.000 0.119 0.687EC 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 26 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
## C_W1_GAD_total ~
## A_PRE_GAD (c1) 0.135 0.012 11.312 0.000 0.135 0.657
## IUS_B1W_change ~
## A_PRE_GAD (a1) 0.027 0.017 1.622 0.105 0.027 0.131
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.503 0.039 13.030 0.000 0.503 0.503
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -2.035 0.166 -12.255 0.000 -2.035 -2.055
## .IUS_B1W_change -0.405 0.312 -1.297 0.195 -0.405 -0.409
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.224 0.053 4.212 0.000 0.224 0.228
## .IUS_B1W_change 0.963 0.469 2.055 0.040 0.963 0.983
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.772
## IUS_B1W_change 0.017
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.014 0.009 1.595 0.111 0.014 0.066
## direct 0.135 0.012 11.312 0.000 0.135 0.657
## total 0.149 0.013 11.412 0.000 0.149 0.723Full 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 16 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## GAD_B1M_change ~
## Group (c1) -0.004 0.062 -0.068 0.946 -0.004 -0.003
## IUS_B1M_change ~
## Group (a1) -0.209 0.083 -2.526 0.012 -0.209 -0.155
## GAD_B1M_change ~
## IUS_B1M_c (b1) 0.675 0.050 13.451 0.000 0.675 0.675
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.009 0.151 0.062 0.950 0.009 0.009
## .IUS_B1M_change 0.461 0.192 2.396 0.017 0.461 0.462
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.541 0.068 7.980 0.000 0.541 0.544
## .IUS_B1M_change 0.972 0.122 7.988 0.000 0.972 0.976
##
## R-Square:
## Estimate
## GAD_B1M_change 0.456
## IUS_B1M_change 0.024
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.141 0.056 -2.517 0.012 -0.141 -0.105
## direct -0.004 0.062 -0.068 0.946 -0.004 -0.003
## total -0.145 0.083 -1.750 0.080 -0.145 -0.108Intervention 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 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 103
## 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
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.100 0.011 9.396 0.000 0.100 0.546
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.004 0.014 -0.268 0.788 -0.004 -0.021
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.572 0.075 7.597 0.000 0.572 0.572
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.622 0.170 -9.555 0.000 -1.622 -1.630
## .IUS_B1M_change 0.063 0.247 0.256 0.798 0.063 0.063
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.384 0.062 6.200 0.000 0.384 0.388
## .IUS_B1M_change 0.990 0.184 5.379 0.000 0.990 1.000
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.612
## IUS_B1M_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.002 0.008 -0.266 0.790 -0.002 -0.012
## direct 0.100 0.011 9.396 0.000 0.100 0.546
## total 0.097 0.013 7.632 0.000 0.097 0.534Psychoeducation 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 28 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
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.072 0.010 7.252 0.000 0.072 0.417
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.025 0.019 -1.300 0.194 -0.025 -0.143
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.671 0.065 10.368 0.000 0.671 0.671
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.117 0.154 -7.270 0.000 -1.117 -1.122
## .IUS_B1M_change 0.383 0.272 1.405 0.160 0.383 0.384
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.452 0.075 5.988 0.000 0.452 0.456
## .IUS_B1M_change 0.970 0.180 5.388 0.000 0.970 0.980
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.544
## IUS_B1M_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.017 0.013 -1.247 0.213 -0.017 -0.096
## direct 0.072 0.010 7.252 0.000 0.072 0.417
## total 0.056 0.017 3.219 0.001 0.056 0.321EC 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 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
## D_M1_GAD_total ~
## A_PRE_GAD (c1) 0.067 0.024 2.783 0.005 0.067 0.327
## IUS_B1M_change ~
## A_PRE_GAD (a1) 0.002 0.018 0.122 0.903 0.002 0.011
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.673 0.051 13.210 0.000 0.673 0.673
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.013 0.366 -2.765 0.006 -1.013 -1.023
## .IUS_B1M_change -0.033 0.294 -0.113 0.910 -0.033 -0.034
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.427 0.142 3.008 0.003 0.427 0.436
## .IUS_B1M_change 0.980 0.301 3.256 0.001 0.980 1.000
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.564
## IUS_B1M_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.001 0.012 0.122 0.903 0.001 0.007
## direct 0.067 0.024 2.783 0.005 0.067 0.327
## total 0.069 0.027 2.562 0.010 0.069 0.334H3: 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 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## Mood_BP_change ~
## Group (c1) 0.320 0.077 4.160 0.000 0.320 0.236
## IUS_BP_change ~
## Group (a1) -0.419 0.079 -5.274 0.000 -0.419 -0.311
## Mood_BP_change ~
## IUS_BP_ch (b1) -0.241 0.077 -3.154 0.002 -0.241 -0.240
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change -0.699 0.166 -4.212 0.000 -0.699 -0.697
## .IUS_BP_change 0.923 0.159 5.816 0.000 0.923 0.924
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change 0.855 0.144 5.952 0.000 0.855 0.851
## .IUS_BP_change 0.900 0.139 6.490 0.000 0.900 0.903
##
## R-Square:
## Estimate
## Mood_BP_change 0.149
## IUS_BP_change 0.097
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.101 0.034 2.984 0.003 0.101 0.075
## direct 0.320 0.077 4.160 0.000 0.320 0.236
## total 0.421 0.072 5.811 0.000 0.421 0.311Intervention 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 17 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
## B_POST_mood_mean ~
## A_PRE_md_ (c1) 0.014 0.003 4.715 0.000 0.014 0.600
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.530 0.596 0.001 0.051
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.162 0.093 -1.751 0.080 -0.162 -0.162
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.407 0.147 -2.767 0.006 -0.407 -0.407
## .IUS_BP_change -0.035 0.099 -0.357 0.721 -0.035 -0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.624 0.128 4.889 0.000 0.624 0.624
## .IUS_BP_change 0.988 0.224 4.416 0.000 0.988 0.997
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.376
## IUS_BP_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.495 0.621 -0.000 -0.008
## direct 0.014 0.003 4.715 0.000 0.014 0.600
## total 0.014 0.003 4.724 0.000 0.014 0.591Psychoeducation 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 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
## Mood_B1W_change ~
## Group (c1) 0.020 0.088 0.224 0.823 0.020 0.015
## IUS_B1W_change ~
## Group (a1) -0.269 0.085 -3.152 0.002 -0.269 -0.199
## Mood_B1W_change ~
## IUS_B1W_c (b1) -0.211 0.094 -2.246 0.025 -0.211 -0.209
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang -0.021 0.207 -0.102 0.919 -0.021 -0.021
## .IUS_B1W_change 0.592 0.196 3.018 0.003 0.592 0.593
##
## 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 0.955
## .IUS_B1W_change 0.957 0.158 6.071 0.000 0.957 0.960
##
## R-Square:
## Estimate
## Mood_B1W_chang 0.045
## IUS_B1W_change 0.040
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.057 0.030 1.914 0.056 0.057 0.042
## direct 0.020 0.088 0.224 0.823 0.020 0.015
## total 0.076 0.082 0.929 0.353 0.076 0.056Intervention 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 17 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_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) 0.001 0.002 0.492 0.623 0.001 0.051
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.133 0.114 -1.173 0.241 -0.133 -0.134
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.311 0.110 -2.833 0.005 -0.311 -0.313
## .IUS_B1W_change -0.036 0.114 -0.311 0.756 -0.036 -0.036
##
## 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 0.988 0.220 4.496 0.000 0.988 0.997
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.221
## IUS_B1W_change 0.003
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.478 0.633 -0.000 -0.007
## direct 0.011 0.002 4.421 0.000 0.011 0.457
## total 0.011 0.003 4.150 0.000 0.011 0.450Psychoeducation 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 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## 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) 0.010 0.002 4.478 0.000 0.010 0.444
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.633 0.527 0.001 0.062
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.198 0.120 -1.648 0.099 -0.198 -0.196
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.361 0.135 -2.670 0.008 -0.361 -0.359
## .IUS_B1W_change -0.053 0.133 -0.397 0.691 -0.053 -0.053
##
## 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 0.776
## .IUS_B1W_change 0.987 0.235 4.206 0.000 0.987 0.996
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.224
## IUS_B1W_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.554 0.579 -0.000 -0.012
## direct 0.010 0.002 4.478 0.000 0.010 0.444
## total 0.010 0.002 4.164 0.000 0.010 0.431EC 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 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_mood_mean ~
## A_PRE_md_ (c1) 0.011 0.003 3.198 0.001 0.011 0.434
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.003 0.002 2.046 0.041 0.003 0.136
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.078 0.244 -0.320 0.749 -0.078 -0.078
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.434 0.178 -2.433 0.015 -0.434 -0.439
## .IUS_B1W_change -0.140 0.154 -0.906 0.365 -0.140 -0.141
##
## 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 0.815
## .IUS_B1W_change 0.962 0.476 2.021 0.043 0.962 0.982
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.185
## IUS_B1W_change 0.018
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.314 0.754 -0.000 -0.011
## direct 0.011 0.003 3.198 0.001 0.011 0.434
## total 0.011 0.003 3.239 0.001 0.011 0.424Full 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 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## 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
## Mood_B1M_change ~
## Group (c1) 0.025 0.089 0.281 0.779 0.025 0.017
## IUS_B1M_change ~
## Group (a1) -0.209 0.083 -2.526 0.012 -0.209 -0.155
## Mood_B1M_change ~
## IUS_B1M_c (b1) -0.433 0.126 -3.450 0.001 -0.433 -0.409
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.064 0.219 0.293 0.770 0.064 0.061
## .IUS_B1M_change 0.461 0.192 2.396 0.017 0.461 0.462
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.926 0.104 8.930 0.000 0.926 0.830
## .IUS_B1M_change 0.972 0.122 7.988 0.000 0.972 0.976
##
## R-Square:
## Estimate
## Mood_B1M_chang 0.170
## IUS_B1M_change 0.024
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.090 0.044 2.067 0.039 0.090 0.063
## direct 0.025 0.089 0.281 0.779 0.025 0.017
## total 0.115 0.089 1.296 0.195 0.115 0.081Intervention 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 13 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
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.288 0.000 0.010 0.417
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.003 0.002 1.160 0.246 0.003 0.111
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.329 0.135 -2.428 0.015 -0.329 -0.320
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.209 0.115 -1.806 0.071 -0.209 -0.204
## .IUS_B1M_change -0.077 0.120 -0.645 0.519 -0.077 -0.078
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.785 0.140 5.613 0.000 0.785 0.753
## .IUS_B1M_change 0.978 0.183 5.338 0.000 0.978 0.988
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.247
## IUS_B1M_change 0.012
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.001 -1.014 0.311 -0.001 -0.036
## direct 0.010 0.002 4.288 0.000 0.010 0.417
## total 0.009 0.003 3.594 0.000 0.009 0.381Psychoeducation 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 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 106
## 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
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.010 0.002 4.816 0.000 0.010 0.422
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.001 0.002 0.786 0.432 0.001 0.060
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.342 0.145 -2.365 0.018 -0.342 -0.332
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.274 0.131 -2.092 0.036 -0.274 -0.268
## .IUS_B1M_change -0.051 0.121 -0.423 0.672 -0.051 -0.051
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.764 0.093 8.253 0.000 0.764 0.728
## .IUS_B1M_change 0.987 0.197 5.013 0.000 0.987 0.996
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.272
## IUS_B1M_change 0.004
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.001 -0.698 0.485 -0.000 -0.020
## direct 0.010 0.002 4.816 0.000 0.010 0.422
## total 0.009 0.002 4.193 0.000 0.009 0.403EC 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 26 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
## D_M1_mood_mean ~
## A_PRE_md_ (c1) 0.016 0.002 7.150 0.000 0.016 0.594
## IUS_B1M_change ~
## A_PRE_md_ (a1) -0.001 0.003 -0.189 0.850 -0.001 -0.024
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.294 0.292 -1.007 0.314 -0.294 -0.278
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.562 0.206 -2.725 0.006 -0.562 -0.538
## .IUS_B1M_change 0.025 0.177 0.142 0.887 0.025 0.025
##
## 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 0.562
## .IUS_B1M_change 0.979 0.300 3.266 0.001 0.979 0.999
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.438
## IUS_B1M_change 0.001
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.000 0.001 0.187 0.852 0.000 0.007
## direct 0.016 0.002 7.150 0.000 0.016 0.594
## total 0.016 0.002 6.726 0.000 0.016 0.600Exploratory 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
## -20.0159 -2.0823 0.7306 3.0574 12.9841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.9985 1.6404 -1.828 0.0687 .
## GroupB_Controls 0.8065 2.0501 0.393 0.6944
## GroupC_Intervention 1.1225 2.0429 0.549 0.5832
## A_PRE_GM 0.8170 0.5345 1.529 0.1276
## GroupB_Controls:A_PRE_GM -0.5426 0.6425 -0.845 0.3991
## GroupC_Intervention:A_PRE_GM -0.8530 0.6558 -1.301 0.1946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.226 on 253 degrees of freedom
## Multiple R-squared: 0.01907, Adjusted R-squared: -0.0003146
## F-statistic: 0.9838 on 5 and 253 DF, p-value: 0.4282anova(moderation_GM_PHQ_1W)## Analysis of Variance Table
##
## Response: PHQ_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 54.1 27.054 0.9905 0.3728
## A_PRE_GM 1 34.0 33.971 1.2438 0.2658
## Group:A_PRE_GM 2 46.3 23.135 0.8470 0.4299
## Residuals 253 6910.3 27.313# 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
## -25.700 -3.261 1.451 4.711 22.300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.6831 2.5196 -0.668 0.505
## GroupB_Controls -2.3436 3.1490 -0.744 0.457
## GroupC_Intervention -0.8238 3.1380 -0.263 0.793
## A_PRE_GM -0.1813 0.8209 -0.221 0.825
## GroupB_Controls:A_PRE_GM 0.4691 0.9868 0.475 0.635
## GroupC_Intervention:A_PRE_GM -0.4066 1.0074 -0.404 0.687
##
## Residual standard error: 8.028 on 253 degrees of freedom
## Multiple R-squared: 0.01432, Adjusted R-squared: -0.00516
## F-statistic: 0.7351 on 5 and 253 DF, p-value: 0.5977anova(moderation_GM_PHQ_1M)## Analysis of Variance Table
##
## Response: PHQ_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 150.6 75.293 1.1684 0.3125
## A_PRE_GM 1 8.9 8.889 0.1379 0.7106
## Group:A_PRE_GM 2 77.4 38.696 0.6005 0.5493
## Residuals 253 16303.8 64.442E1: 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
## -18.5676 -2.0544 0.4949 2.4807 15.5291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1403 1.5552 -1.376 0.1700
## GroupB_Controls 2.5470 1.9436 1.310 0.1912
## GroupC_Intervention 0.5145 1.9368 0.266 0.7907
## A_PRE_GM 0.6863 0.5067 1.354 0.1768
## GroupB_Controls:A_PRE_GM -1.1370 0.6091 -1.867 0.0631 .
## GroupC_Intervention:A_PRE_GM -0.6379 0.6218 -1.026 0.3059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.955 on 253 degrees of freedom
## Multiple R-squared: 0.02205, Adjusted R-squared: 0.002722
## F-statistic: 1.141 on 5 and 253 DF, p-value: 0.3392anova(moderation_GM_GAD_1W)## Analysis of Variance Table
##
## Response: GAD_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 50.9 25.452 1.0367 0.3561
## A_PRE_GM 1 1.0 0.977 0.0398 0.8420
## Group:A_PRE_GM 2 88.2 44.079 1.7955 0.1682
## Residuals 253 6211.2 24.550# 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
## -25.151 -2.289 1.296 4.085 19.507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7183 2.2558 -0.762 0.447
## GroupB_Controls -0.9127 2.8193 -0.324 0.746
## GroupC_Intervention 0.4863 2.8094 0.173 0.863
## A_PRE_GM 0.2110 0.7350 0.287 0.774
## GroupB_Controls:A_PRE_GM -0.2474 0.8835 -0.280 0.780
## GroupC_Intervention:A_PRE_GM -0.9505 0.9019 -1.054 0.293
##
## Residual standard error: 7.187 on 253 degrees of freedom
## Multiple R-squared: 0.02077, Adjusted R-squared: 0.001419
## F-statistic: 1.073 on 5 and 253 DF, p-value: 0.3756anova(moderation_GM_GAD_1M)## Analysis of Variance Table
##
## Response: GAD_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 169.3 84.646 1.6387 0.1963
## A_PRE_GM 1 32.5 32.506 0.6293 0.4284
## Group:A_PRE_GM 2 75.4 37.704 0.7299 0.4829
## Residuals 253 13068.5 51.654E1: 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
## -20.4978 -4.2115 0.3611 5.1474 16.9780
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.5947 1.7806 13.25 <2e-16 ***
## A_PRE_FI_total 1.2379 0.1141 10.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.354 on 257 degrees of freedom
## Multiple R-squared: 0.314, Adjusted R-squared: 0.3113
## F-statistic: 117.6 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 6360.1 6360.1 117.61 < 2.2e-16 ***
## Residuals 257 13897.6 54.1
## ---
## 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: 4585.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5128 -0.3758 0.0291 0.5036 2.2680
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.546 3.090
## Residual 15.332 3.916
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.86000 0.70538 593.28918 21.067
## GroupB_Controls 0.09283 0.85573 593.28918 0.108
## GroupC_Intervention 0.45068 0.85971 593.28918 0.524
## TimeC_W1_FI_total -0.48000 0.78312 512.00000 -0.613
## TimeD_M1_FI_total -1.90000 0.78312 512.00000 -2.426
## GroupB_Controls:TimeC_W1_FI_total -0.07660 0.95003 512.00000 -0.081
## GroupC_Intervention:TimeC_W1_FI_total -0.86951 0.95446 512.00000 -0.911
## GroupB_Controls:TimeD_M1_FI_total -0.35472 0.95003 512.00000 -0.373
## GroupC_Intervention:TimeD_M1_FI_total -0.73107 0.95446 512.00000 -0.766
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.9137
## GroupC_Intervention 0.6003
## TimeC_W1_FI_total 0.5402
## TimeD_M1_FI_total 0.0156 *
## GroupB_Controls:TimeC_W1_FI_total 0.9358
## GroupC_Intervention:TimeC_W1_FI_total 0.3627
## GroupB_Controls:TimeD_M1_FI_total 0.7090
## GroupC_Intervention:TimeD_M1_FI_total 0.4441
## ---
## 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 -0.555 0.458 0.455
## TmD_M1_FI_t -0.555 0.458 0.455 0.500
## GB_C:TC_W1_ 0.458 -0.555 -0.375 -0.824 -0.412
## GC_I:TC_W1_ 0.455 -0.375 -0.555 -0.820 -0.410 0.676
## GB_C:TD_M1_ 0.458 -0.555 -0.375 -0.412 -0.824 0.500 0.338
## GC_I:TD_M1_ 0.455 -0.375 -0.555 -0.410 -0.820 0.338 0.500 0.676anova (FI_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.24 0.122 2 256 0.0079 0.9921
## Time 605.46 302.728 2 512 19.7448 5.49e-09 ***
## Group:Time 23.37 5.842 4 512 0.3811 0.8222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.86 | 13.48 – 16.24 | <0.001 |
| Group [B_Controls] | 0.09 | -1.59 – 1.77 | 0.914 |
| Group [C_Intervention] | 0.45 | -1.24 – 2.14 | 0.600 |
| Time [C_W1_FI_total] | -0.48 | -2.02 – 1.06 | 0.540 |
| Time [D_M1_FI_total] | -1.90 | -3.44 – -0.36 | 0.015 |
|
Group [B_Controls] × Time [C_W1_FI_total] |
-0.08 | -1.94 – 1.79 | 0.936 |
|
Group [C_Intervention] × Time [C_W1_FI_total] |
-0.87 | -2.74 – 1.00 | 0.363 |
|
Group [B_Controls] × Time [D_M1_FI_total] |
-0.35 | -2.22 – 1.51 | 0.709 |
|
Group [C_Intervention] × Time [D_M1_FI_total] |
-0.73 | -2.60 – 1.14 | 0.444 |
| Random Effects | |||
| σ2 | 15.33 | ||
| Ï„00 ID | 9.55 | ||
| ICC | 0.38 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.037 / 0.407 | ||
parameters::standardise_parameters(FI_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.17 | [-0.11, 0.44]
## GroupB_Controls | 0.02 | [-0.31, 0.35]
## GroupC_Intervention | 0.09 | [-0.24, 0.42]
## TimeC_W1_FI_total | -0.09 | [-0.40, 0.21]
## TimeD_M1_FI_total | -0.38 | [-0.68, -0.07]
## GroupB_Controls:TimeC_W1_FI_total | -0.02 | [-0.38, 0.35]
## GroupC_Intervention:TimeC_W1_FI_total | -0.17 | [-0.54, 0.20]
## GroupB_Controls:TimeD_M1_FI_total | -0.07 | [-0.44, 0.30]
## GroupC_Intervention:TimeD_M1_FI_total | -0.14 | [-0.51, 0.23]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: 2917.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6197 -0.4865 0.0175 0.4849 2.7271
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.554 3.091
## Residual 9.586 3.096
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.86000 0.61871 409.87229 24.018
## GroupB_Controls 0.09283 0.75058 409.87229 0.124
## GroupC_Intervention 0.45068 0.75408 409.87229 0.598
## TimeC_W1_FI_total -0.48000 0.61923 256.00000 -0.775
## GroupB_Controls:TimeC_W1_FI_total -0.07660 0.75121 256.00000 -0.102
## GroupC_Intervention:TimeC_W1_FI_total -0.86951 0.75470 256.00000 -1.152
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.902
## GroupC_Intervention 0.550
## TimeC_W1_FI_total 0.439
## GroupB_Controls:TimeC_W1_FI_total 0.919
## GroupC_Intervention:TimeC_W1_FI_total 0.250
## ---
## 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 -0.500 0.412 0.411
## GB_C:TC_W1_ 0.412 -0.500 -0.338 -0.824
## GC_I:TC_W1_ 0.411 -0.338 -0.500 -0.820 0.676anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.086 0.043 2 256 0.0045 0.995524
## Time 72.728 72.728 1 256 7.5869 0.006301 **
## Group:Time 20.828 10.414 2 256 1.0864 0.338986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM_B1W)| Â | FI Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 14.86 | 13.64 – 16.08 | <0.001 |
| Group [B_Controls] | 0.09 | -1.38 – 1.57 | 0.902 |
| Group [C_Intervention] | 0.45 | -1.03 – 1.93 | 0.550 |
| Time [C_W1_FI_total] | -0.48 | -1.70 – 0.74 | 0.439 |
|
Group [B_Controls] × Time [C_W1_FI_total] |
-0.08 | -1.55 – 1.40 | 0.919 |
|
Group [C_Intervention] × Time [C_W1_FI_total] |
-0.87 | -2.35 – 0.61 | 0.250 |
| Random Effects | |||
| σ2 | 9.59 | ||
| Ï„00 ID | 9.55 | ||
| ICC | 0.50 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.505 | ||
parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.05 | [-0.23, 0.33]
## GroupB_Controls | 0.02 | [-0.32, 0.36]
## GroupC_Intervention | 0.10 | [-0.24, 0.44]
## TimeC_W1_FI_total | -0.11 | [-0.39, 0.17]
## GroupB_Controls:TimeC_W1_FI_total | -0.02 | [-0.35, 0.32]
## GroupC_Intervention:TimeC_W1_FI_total | -0.20 | [-0.54, 0.14]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: 3116.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73980 -0.43311 0.05596 0.52370 2.09643
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.596 3.098
## Residual 16.669 4.083
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 14.86000 0.72477 451.70879 20.503
## GroupB_Controls 0.09283 0.87925 451.70879 0.106
## GroupC_Intervention 0.45068 0.88334 451.70879 0.510
## TimeD_M1_FI_total -1.90000 0.81656 256.00000 -2.327
## GroupB_Controls:TimeD_M1_FI_total -0.35472 0.99059 256.00000 -0.358
## GroupC_Intervention:TimeD_M1_FI_total -0.73107 0.99521 256.00000 -0.735
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.9160
## GroupC_Intervention 0.6102
## TimeD_M1_FI_total 0.0208 *
## GroupB_Controls:TimeD_M1_FI_total 0.7206
## GroupC_Intervention:TimeD_M1_FI_total 0.4633
## ---
## 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 -0.563 0.464 0.462
## GB_C:TD_M1_ 0.464 -0.563 -0.381 -0.824
## GC_I:TD_M1_ 0.462 -0.381 -0.563 -0.820 0.676anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.40 0.70 2 256 0.0419 0.9589
## Time 588.19 588.19 1 256 35.2863 9.248e-09 ***
## Group:Time 9.59 4.79 2 256 0.2875 0.7503
## ---
## 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) | 14.86 | 13.44 – 16.28 | <0.001 |
| Group [B_Controls] | 0.09 | -1.63 – 1.82 | 0.916 |
| Group [C_Intervention] | 0.45 | -1.28 – 2.19 | 0.610 |
| Time [D_M1_FI_total] | -1.90 | -3.50 – -0.30 | 0.020 |
|
Group [B_Controls] × Time [D_M1_FI_total] |
-0.35 | -2.30 – 1.59 | 0.720 |
|
Group [C_Intervention] × Time [D_M1_FI_total] |
-0.73 | -2.69 – 1.22 | 0.463 |
| Random Effects | |||
| σ2 | 16.67 | ||
| Ï„00 ID | 9.60 | ||
| ICC | 0.37 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.050 / 0.397 | ||
parameters::standardise_parameters(FI_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.18 | [-0.09, 0.45]
## GroupB_Controls | 0.02 | [-0.31, 0.35]
## GroupC_Intervention | 0.09 | [-0.25, 0.42]
## TimeD_M1_FI_total | -0.36 | [-0.67, -0.06]
## GroupB_Controls:TimeD_M1_FI_total | -0.07 | [-0.44, 0.30]
## GroupC_Intervention:TimeD_M1_FI_total | -0.14 | [-0.51, 0.23]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'