Grouped Analyses
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## stepSet 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)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_distressed_pleasant 1 1379.6 1379.58 18.781 2.105e-05 ***
## Residuals 257 18878.1 73.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#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)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## B_anxious_relaxed 1 2093.8 2093.83 29.564 1.262e-07 ***
## Residuals 256 18130.7 70.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#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)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_mood_mean 1 2072.2 2072.24 29.285 1.431e-07 ***
## Residuals 257 18185.4 70.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1H1a: 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
Hypothesis 2 - Grouped
Grouping the modules vs the extra controls
Full_data_all$Group[which(Full_data_all$Group == "C_Intervention")]<-1
Full_data_all$Group[which(Full_data_all$Group == "B_Controls")]<-1
Full_data_all$Group[which(Full_data_all$Group == "A_ECs")]<-0H2a: 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: 7823.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4183 -0.3355 0.0620 0.4953 2.3068
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 72.54 8.517
## Residual 77.24 8.788
## Number of obs: 1036, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.731 603.380 23.735 <2e-16 ***
## Group1 1.461 1.927 603.380 0.758 0.4487
## TimeB_POST_IUS_total -0.280 1.758 771.000 -0.159 0.8735
## TimeC_W1_IUS_total -0.720 1.758 771.000 -0.410 0.6822
## TimeD_M1_IUS_total -2.900 1.758 771.000 -1.650 0.0994 .
## Group1:TimeB_POST_IUS_total -5.131 1.957 771.000 -2.623 0.0089 **
## Group1:TimeC_W1_IUS_total -3.237 1.957 771.000 -1.654 0.0985 .
## Group1:TimeD_M1_IUS_total -4.727 1.957 771.000 -2.416 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS TC_W1_ TD_M1_ G1:TB_ G1:TC_
## Group1 -0.898
## TB_POST_IUS -0.508 0.456
## TmC_W1_IUS_ -0.508 0.456 0.500
## TmD_M1_IUS_ -0.508 0.456 0.500 0.500
## G1:TB_POST_ 0.456 -0.508 -0.898 -0.449 -0.449
## G1:TC_W1_IU 0.456 -0.508 -0.449 -0.898 -0.449 0.500
## G1:TD_M1_IU 0.456 -0.508 -0.449 -0.449 -0.898 0.500 0.500anova (IUS_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 111.53 111.53 1 257 1.4440 0.23059
## Time 2256.54 752.18 3 771 9.7386 2.596e-06 ***
## Group:Time 656.91 218.97 3 771 2.8350 0.03734 *
## ---
## 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 [1] | 1.46 | -2.32 – 5.24 | 0.449 |
| 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 [1] × Time [B_POST_IUS_total] |
-5.13 | -8.97 – -1.29 | 0.009 |
|
Group [1] × Time [C_W1_IUS_total] |
-3.24 | -7.08 – 0.60 | 0.098 |
|
Group [1] × Time [D_M1_IUS_total] |
-4.73 | -8.57 – -0.89 | 0.016 |
| Random Effects | |||
| σ2 | 77.24 | ||
| Ï„00 ID | 72.54 | ||
| ICC | 0.48 | ||
| N ID | 259 | ||
| Observations | 1036 | ||
| Marginal R2 / Conditional R2 | 0.045 / 0.507 | ||
parameters::standardise_parameters(IUS_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.20 | [-0.08, 0.47]
## Group1 | 0.12 | [-0.19, 0.42]
## 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]
## Group1:TimeB_POST_IUS_total | -0.41 | [-0.72, -0.10]
## Group1:TimeC_W1_IUS_total | -0.26 | [-0.57, 0.05]
## Group1:TimeD_M1_IUS_total | -0.38 | [-0.69, -0.07]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: 3669.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3396 -0.4364 0.0214 0.4075 3.3019
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 70.86 8.418
## Residual 29.62 5.442
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.418 343.274 28.978 < 2e-16 ***
## Group1 1.461 1.578 343.274 0.926 0.355
## TimeB_POST_IUS_total -0.280 1.089 257.000 -0.257 0.797
## Group1:TimeB_POST_IUS_total -5.131 1.212 257.000 -4.235 3.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS
## Group1 -0.898
## TB_POST_IUS -0.384 0.345
## G1:TB_POST_ 0.345 -0.384 -0.898anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 17.03 17.03 1 257 0.5751 0.4489
## Time 653.49 653.49 1 257 22.0624 4.302e-06 ***
## Group:Time 531.22 531.22 1 257 17.9344 3.187e-05 ***
## ---
## 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 | 38.29 – 43.87 | <0.001 |
| Group [1] | 1.46 | -1.64 – 4.56 | 0.355 |
| Time [B_POST_IUS_total] | -0.28 | -2.42 – 1.86 | 0.797 |
|
Group [1] × Time [B_POST_IUS_total] |
-5.13 | -7.51 – -2.75 | <0.001 |
| Random Effects | |||
| σ2 | 29.62 | ||
| Ï„00 ID | 70.86 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.057 / 0.722 | ||
parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.37]
## Group1 | 0.14 | [-0.16, 0.44]
## TimeB_POST_IUS_total | -0.03 | [-0.23, 0.18]
## Group1:TimeB_POST_IUS_total | -0.50 | [-0.73, -0.27]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: 3756.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9648 -0.3595 0.0595 0.4288 2.5626
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 68.94 8.303
## Residual 39.99 6.324
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.476 367.008 27.831 <2e-16 ***
## Group1 1.461 1.643 367.008 0.889 0.3746
## TimeC_W1_IUS_total -0.720 1.265 257.000 -0.569 0.5697
## Group1:TimeC_W1_IUS_total -3.237 1.408 257.000 -2.299 0.0223 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TmC_W1_IUS_ -0.428 0.385
## G1:TC_W1_IU 0.385 -0.428 -0.898anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.45 0.45 1 257 0.0113 0.915439
## Time 441.28 441.28 1 257 11.0337 0.001025 **
## Group:Time 211.38 211.38 1 257 5.2852 0.022310 *
## ---
## 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 [1] | 1.46 | -1.77 – 4.69 | 0.374 |
| Time [C_W1_IUS_total] | -0.72 | -3.20 – 1.76 | 0.569 |
|
Group [1] × Time [C_W1_IUS_total] |
-3.24 | -6.00 – -0.47 | 0.022 |
| Random Effects | |||
| σ2 | 39.99 | ||
| Ï„00 ID | 68.94 | ||
| ICC | 0.63 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.028 / 0.643 | ||
parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------
## (Intercept) | 0.05 | [-0.23, 0.32]
## Group1 | 0.14 | [-0.17, 0.44]
## TimeC_W1_IUS_total | -0.07 | [-0.30, 0.17]
## Group1:TimeC_W1_IUS_total | -0.31 | [-0.57, -0.04]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: 4079
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92290 -0.36731 0.08071 0.53869 1.81932
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.19 7.693
## Residual 109.45 10.462
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.836 457.626 22.369 <2e-16 ***
## Group1 1.461 2.044 457.626 0.714 0.4753
## TimeD_M1_IUS_total -2.900 2.092 257.000 -1.386 0.1669
## Group1:TimeD_M1_IUS_total -4.727 2.329 257.000 -2.029 0.0435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TmD_M1_IUS_ -0.570 0.512
## G1:TD_M1_IU 0.512 -0.570 -0.898anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 31.59 31.59 1 257 0.2886 0.59155
## Time 2235.52 2235.52 1 257 20.4257 9.464e-06 ***
## Group:Time 450.73 450.73 1 257 4.1183 0.04345 *
## ---
## 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 [1] | 1.46 | -2.56 – 5.48 | 0.475 |
| Time [D_M1_IUS_total] | -2.90 | -7.01 – 1.21 | 0.166 |
|
Group [1] × Time [D_M1_IUS_total] |
-4.73 | -9.30 – -0.15 | 0.043 |
| Random Effects | |||
| σ2 | 109.45 | ||
| Ï„00 ID | 59.19 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.068 / 0.395 | ||
parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------
## (Intercept) | 0.16 | [-0.11, 0.43]
## Group1 | 0.11 | [-0.19, 0.41]
## TimeD_M1_IUS_total | -0.22 | [-0.52, 0.09]
## Group1:TimeD_M1_IUS_total | -0.35 | [-0.69, -0.01]plot_model(IUS_MEM_B1M, 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: 3111.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0685 -0.5136 -0.0799 0.4400 3.7636
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.3393 1.1573
## Residual 0.7765 0.8812
## Number of obs: 996, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2057 461.0841 13.320 < 2e-16 ***
## Group1 0.2839 0.2290 461.0841 1.240 0.21567
## TimeB_POST_GM 0.0400 0.1762 731.3450 0.227 0.82052
## TimeC_W1_GM -0.0759 0.1786 732.8977 -0.425 0.67096
## TimeD_M1_GM -0.0398 0.1838 736.1440 -0.217 0.82860
## Group1:TimeB_POST_GM -0.6192 0.1963 731.4161 -3.154 0.00167 **
## Group1:TimeC_W1_GM -0.3733 0.1986 732.8407 -1.879 0.06058 .
## Group1:TimeD_M1_GM -0.4680 0.2046 736.1991 -2.288 0.02243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS TC_W1_ TD_M1_ G1:TB_ G1:TC_
## Group1 -0.898
## TmB_POST_GM -0.428 0.385
## TimeC_W1_GM -0.423 0.380 0.493
## TimeD_M1_GM -0.411 0.369 0.480 0.472
## G1:TB_POST_ 0.385 -0.428 -0.898 -0.443 -0.430
## G1:TC_W1_GM 0.380 -0.423 -0.444 -0.899 -0.425 0.494
## G1:TD_M1_GM 0.369 -0.411 -0.431 -0.424 -0.898 0.479 0.473anova (GM_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.1338 0.13378 1 257.46 0.1723 0.67844
## Time 8.5844 2.86148 3 734.47 3.6849 0.01184 *
## Group:Time 8.3416 2.78052 3 734.47 3.5806 0.01364 *
## ---
## 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 [1] | 0.28 | -0.17 – 0.73 | 0.215 |
| 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 | 0.829 |
|
Group [1] × Time [B_POST_GM] |
-0.62 | -1.00 – -0.23 | 0.002 |
|
Group [1] × Time [C_W1_GM] |
-0.37 | -0.76 – 0.02 | 0.060 |
|
Group [1] × Time [D_M1_GM] |
-0.47 | -0.87 – -0.07 | 0.022 |
| Random Effects | |||
| σ2 | 0.78 | ||
| Ï„00 ID | 1.34 | ||
| ICC | 0.63 | ||
| N ID | 259 | ||
| Observations | 996 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.640 | ||
parameters::standardise_parameters(GM_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.05 | [-0.22, 0.33]
## Group1 | 0.19 | [-0.11, 0.50]
## 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]
## Group1:TimeB_POST_GM | -0.42 | [-0.69, -0.16]
## Group1:TimeC_W1_GM | -0.25 | [-0.52, 0.01]
## Group1:TimeD_M1_GM | -0.32 | [-0.59, -0.05]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: 1674.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64644 -0.53252 -0.00609 0.43121 2.98242
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.4862 1.2191
## Residual 0.6154 0.7844
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2050 342.4179 13.365 < 2e-16 ***
## Group1 0.2839 0.2282 342.4179 1.244 0.214326
## TimeB_POST_GM 0.0400 0.1569 255.5424 0.255 0.798961
## Group1:TimeB_POST_GM -0.6245 0.1748 255.6612 -3.573 0.000422 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS
## Group1 -0.898
## TmB_POST_GM -0.383 0.344
## G1:TB_POST_ 0.343 -0.382 -0.898anova (GM_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.0111 0.0111 1 257.08 0.0180 0.8933019
## Time 5.9710 5.9710 1 255.66 9.7034 0.0020489 **
## Group:Time 7.8546 7.8546 1 255.66 12.7644 0.0004221 ***
## ---
## 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 [1] | 0.28 | -0.16 – 0.73 | 0.214 |
| Time [B_POST_GM] | 0.04 | -0.27 – 0.35 | 0.799 |
|
Group [1] × Time [B_POST_GM] |
-0.62 | -0.97 – -0.28 | <0.001 |
| Random Effects | |||
| σ2 | 0.62 | ||
| Ï„00 ID | 1.49 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.717 | ||
parameters::standardise_parameters(GM_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 1.11e-03 | [-0.27, 0.28]
## Group1 | 0.19 | [-0.11, 0.50]
## TimeB_POST_GM | 0.03 | [-0.18, 0.24]
## Group1:TimeB_POST_GM | -0.42 | [-0.66, -0.19]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: 1751
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26286 -0.50692 -0.04646 0.49859 2.77796
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.056 1.027
## Residual 1.015 1.007
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.20350 404.26949 13.464 <2e-16 ***
## Group1 0.28392 0.22654 404.26949 1.253 0.211
## TimeC_W1_GM -0.07843 0.20464 254.85261 -0.383 0.702
## Group1:TimeC_W1_GM -0.37245 0.22753 254.55771 -1.637 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TimeC_W1_GM -0.487 0.438
## G1:TC_W1_GM 0.438 -0.488 -0.899anova (GM_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.2477 0.2477 1 258.25 0.2441 0.62170
## Time 5.4932 5.4932 1 254.56 5.4121 0.02078 *
## Group:Time 2.7198 2.7198 1 254.56 2.6797 0.10287
## ---
## 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 [1] | 0.28 | -0.16 – 0.73 | 0.211 |
| Time [C_W1_GM] | -0.08 | -0.48 – 0.32 | 0.702 |
|
Group [1] × Time [C_W1_GM] |
-0.37 | -0.82 – 0.07 | 0.102 |
| Random Effects | |||
| σ2 | 1.01 | ||
| Ï„00 ID | 1.06 | ||
| ICC | 0.51 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.020 / 0.520 | ||
parameters::standardise_parameters(GM_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.03 | [-0.31, 0.24]
## Group1 | 0.20 | [-0.11, 0.50]
## TimeC_W1_GM | -0.05 | [-0.33, 0.22]
## Group1:TimeC_W1_GM | -0.26 | [-0.57, 0.05]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: 1642.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3896 -0.5692 -0.1152 0.4890 2.8604
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.1176 1.057
## Residual 0.8931 0.945
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.20053 375.81950 13.664 <2e-16 ***
## Group1 0.28392 0.22323 375.81950 1.272 0.204
## TimeD_M1_GM -0.03848 0.19878 236.81456 -0.194 0.847
## Group1:TimeD_M1_GM -0.44800 0.22128 236.80802 -2.025 0.044 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TimeD_M1_GM -0.448 0.403
## G1:TD_M1_GM 0.403 -0.448 -0.898anova (GM_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.0803 0.0803 1 255.62 0.0899 0.76453
## Time 5.0265 5.0265 1 236.81 5.6281 0.01847 *
## Group:Time 3.6608 3.6608 1 236.81 4.0990 0.04403 *
## ---
## 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 [1] | 0.28 | -0.15 – 0.72 | 0.204 |
| Time [D_M1_GM] | -0.04 | -0.43 – 0.35 | 0.847 |
|
Group [1] × Time [D_M1_GM] |
-0.45 | -0.88 – -0.01 | 0.043 |
| Random Effects | |||
| σ2 | 0.89 | ||
| Ï„00 ID | 1.12 | ||
| ICC | 0.56 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.024 / 0.566 | ||
parameters::standardise_parameters(GM_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## Group1 | 0.20 | [-0.11, 0.51]
## TimeD_M1_GM | -0.03 | [-0.30, 0.25]
## Group1:TimeD_M1_GM | -0.31 | [-0.62, -0.01]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: 5056.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2413 -0.4385 -0.0231 0.4805 2.5039
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.64 4.543
## Residual 26.70 5.168
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.96000 0.97310 558.62200 18.456 <2e-16 ***
## Group1 0.06871 1.08327 558.62200 0.063 0.9494
## TimeC_W1_PHQ_total -0.76000 1.03351 514.00000 -0.735 0.4625
## TimeD_M1_PHQ_total -2.18000 1.03351 514.00000 -2.109 0.0354 *
## Group1:TimeC_W1_PHQ_total -0.89072 1.15051 514.00000 -0.774 0.4392
## Group1:TimeD_M1_PHQ_total -1.48029 1.15051 514.00000 -1.287 0.1988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_ TD_M1_ G1:TC_
## Group1 -0.898
## TmC_W1_PHQ_ -0.531 0.477
## TmD_M1_PHQ_ -0.531 0.477 0.500
## G1:TC_W1_PH 0.477 -0.531 -0.898 -0.449
## G1:TD_M1_PH 0.477 -0.531 -0.449 -0.898 0.500anova (PHQ_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 18.99 18.99 1 257 0.7112 0.3998
## Time 695.09 347.54 2 514 13.0148 3.063e-06 ***
## Group:Time 44.82 22.41 2 514 0.8391 0.4327
## ---
## 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 [1] | 0.07 | -2.06 – 2.20 | 0.949 |
| Time [C_W1_PHQ_total] | -0.76 | -2.79 – 1.27 | 0.462 |
| Time [D_M1_PHQ_total] | -2.18 | -4.21 – -0.15 | 0.035 |
|
Group [1] × Time [C_W1_PHQ_total] |
-0.89 | -3.15 – 1.37 | 0.439 |
|
Group [1] × Time [D_M1_PHQ_total] |
-1.48 | -3.74 – 0.78 | 0.199 |
| Random Effects | |||
| σ2 | 26.70 | ||
| Ï„00 ID | 20.64 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.041 / 0.459 | ||
parameters::standardise_parameters(PHQ_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------
## (Intercept) | 0.22 | [-0.05, 0.50]
## Group1 | 9.81e-03 | [-0.29, 0.31]
## TimeC_W1_PHQ_total | -0.11 | [-0.40, 0.18]
## TimeD_M1_PHQ_total | -0.31 | [-0.60, -0.02]
## Group1:TimeC_W1_PHQ_total | -0.13 | [-0.45, 0.20]
## Group1:TimeD_M1_PHQ_total | -0.21 | [-0.53, 0.11]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: 3218.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.11171 -0.47607 -0.00757 0.42144 2.76000
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 25.26 5.026
## Residual 13.64 3.694
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.96000 0.88209 361.56212 20.361 <2e-16 ***
## Group1 0.06871 0.98195 361.56212 0.070 0.944
## TimeC_W1_PHQ_total -0.76000 0.73873 257.00000 -1.029 0.305
## Group1:TimeC_W1_PHQ_total -0.89072 0.82237 257.00000 -1.083 0.280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TmC_W1_PHQ_ -0.419 0.376
## G1:TC_W1_PH 0.376 -0.419 -0.898anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.434 2.434 1 257 0.1784 0.673096
## Time 117.241 117.241 1 257 8.5933 0.003678 **
## Group:Time 16.005 16.005 1 257 1.1731 0.279772
## ---
## 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 [1] | 0.07 | -1.86 – 2.00 | 0.944 |
| Time [C_W1_PHQ_total] | -0.76 | -2.21 – 0.69 | 0.304 |
|
Group [1] × Time [C_W1_PHQ_total] |
-0.89 | -2.51 – 0.72 | 0.279 |
| Random Effects | |||
| σ2 | 13.64 | ||
| Ï„00 ID | 25.26 | ||
| ICC | 0.65 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.015 / 0.655 | ||
parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | 0.11 | [-0.17, 0.39]
## Group1 | 0.01 | [-0.30, 0.32]
## TimeC_W1_PHQ_total | -0.12 | [-0.35, 0.11]
## Group1:TimeC_W1_PHQ_total | -0.14 | [-0.40, 0.12]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: 3448.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38625 -0.49407 -0.02521 0.56273 2.24334
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 17.45 4.178
## Residual 32.01 5.658
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.96000 0.99460 457.08843 18.058 <2e-16 ***
## Group1 0.06871 1.10719 457.08843 0.062 0.9505
## TimeD_M1_PHQ_total -2.18000 1.13152 257.00000 -1.927 0.0551 .
## Group1:TimeD_M1_PHQ_total -1.48029 1.25962 257.00000 -1.175 0.2410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TmD_M1_PHQ_ -0.569 0.511
## G1:TD_M1_PH 0.511 -0.569 -0.898anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 17.40 17.40 1 257 0.5437 0.4616
## Time 688.11 688.11 1 257 21.4977 5.642e-06 ***
## Group:Time 44.21 44.21 1 257 1.3811 0.2410
## ---
## 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 [1] | 0.07 | -2.11 – 2.24 | 0.951 |
| Time [D_M1_PHQ_total] | -2.18 | -4.40 – 0.04 | 0.055 |
|
Group [1] × Time [D_M1_PHQ_total] |
-1.48 | -3.95 – 0.99 | 0.240 |
| Random Effects | |||
| σ2 | 32.01 | ||
| Ï„00 ID | 17.45 | ||
| ICC | 0.35 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.057 / 0.390 | ||
parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | 0.23 | [-0.04, 0.50]
## Group1 | 9.51e-03 | [-0.29, 0.31]
## TimeD_M1_PHQ_total | -0.30 | [-0.61, 0.01]
## Group1:TimeD_M1_PHQ_total | -0.20 | [-0.55, 0.14]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: 4922.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4069 -0.4713 0.0113 0.5207 3.2186
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.22 4.384
## Residual 21.74 4.662
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.0200 0.9051 535.2412 16.595 <2e-16 ***
## Group1 0.8460 1.0075 535.2412 0.840 0.4015
## TimeC_W1_GAD_total -0.2600 0.9325 514.0000 -0.279 0.7805
## TimeD_M1_GAD_total -1.1400 0.9325 514.0000 -1.223 0.2221
## Group1:TimeC_W1_GAD_total -0.9840 1.0380 514.0000 -0.948 0.3436
## Group1:TimeD_M1_GAD_total -1.9174 1.0380 514.0000 -1.847 0.0653 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_ TD_M1_ G1:TC_
## Group1 -0.898
## TmC_W1_GAD_ -0.515 0.463
## TmD_M1_GAD_ -0.515 0.463 0.500
## G1:TC_W1_GA 0.463 -0.515 -0.898 -0.449
## G1:TD_M1_GA 0.463 -0.515 -0.449 -0.898 0.500anova (GAD_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.49 0.486 1 257 0.0224 0.8812451
## Time 364.94 182.470 2 514 8.3944 0.0002586 ***
## Group:Time 74.19 37.093 2 514 1.7064 0.1825383
## ---
## 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 [1] | 0.85 | -1.13 – 2.82 | 0.401 |
| Time [C_W1_GAD_total] | -0.26 | -2.09 – 1.57 | 0.780 |
| Time [D_M1_GAD_total] | -1.14 | -2.97 – 0.69 | 0.222 |
|
Group [1] × Time [C_W1_GAD_total] |
-0.98 | -3.02 – 1.05 | 0.343 |
|
Group [1] × Time [D_M1_GAD_total] |
-1.92 | -3.96 – 0.12 | 0.065 |
| Random Effects | |||
| σ2 | 21.74 | ||
| Ï„00 ID | 19.22 | ||
| ICC | 0.47 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.031 / 0.486 | ||
parameters::standardise_parameters(GAD_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | 0.09 | [-0.19, 0.36]
## Group1 | 0.13 | [-0.17, 0.44]
## TimeC_W1_GAD_total | -0.04 | [-0.32, 0.24]
## TimeD_M1_GAD_total | -0.18 | [-0.46, 0.11]
## Group1:TimeC_W1_GAD_total | -0.15 | [-0.47, 0.16]
## Group1:TimeD_M1_GAD_total | -0.30 | [-0.61, 0.02]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: 3160
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.99946 -0.45651 -0.06523 0.46192 2.78438
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.27 4.720
## Residual 12.28 3.504
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.0200 0.8313 363.1154 18.067 <2e-16 ***
## Group1 0.8460 0.9254 363.1154 0.914 0.361
## TimeC_W1_GAD_total -0.2600 0.7009 257.0000 -0.371 0.711
## Group1:TimeC_W1_GAD_total -0.9840 0.7802 257.0000 -1.261 0.208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TmC_W1_GAD_ -0.422 0.379
## G1:TC_W1_GA 0.379 -0.422 -0.898anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.185 2.185 1 257 0.1780 0.67349
## Time 45.634 45.634 1 257 3.7160 0.05499 .
## Group:Time 19.534 19.534 1 257 1.5907 0.20838
## ---
## 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 [1] | 0.85 | -0.97 – 2.66 | 0.361 |
| Time [C_W1_GAD_total] | -0.26 | -1.64 – 1.12 | 0.711 |
|
Group [1] × Time [C_W1_GAD_total] |
-0.98 | -2.52 – 0.55 | 0.208 |
| Random Effects | |||
| σ2 | 12.28 | ||
| Ï„00 ID | 22.27 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.010 / 0.648 | ||
parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## Group1 | 0.14 | [-0.17, 0.45]
## TimeC_W1_GAD_total | -0.04 | [-0.28, 0.19]
## Group1:TimeC_W1_GAD_total | -0.17 | [-0.43, 0.09]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: 3355.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.49057 -0.52258 -0.03535 0.56950 2.43187
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 16.28 4.035
## Residual 25.68 5.067
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.020 0.916 446.740 16.397 <2e-16 ***
## Group1 0.846 1.020 446.740 0.830 0.4072
## TimeD_M1_GAD_total -1.140 1.013 257.000 -1.125 0.2617
## Group1:TimeD_M1_GAD_total -1.917 1.128 257.000 -1.700 0.0904 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TmD_M1_GAD_ -0.553 0.497
## G1:TD_M1_GA 0.497 -0.553 -0.898anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.45 0.45 1 257 0.0176 0.8945808
## Time 355.43 355.43 1 257 13.8429 0.0002441 ***
## Group:Time 74.17 74.17 1 257 2.8887 0.0904151 .
## ---
## 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 [1] | 0.85 | -1.16 – 2.85 | 0.407 |
| Time [D_M1_GAD_total] | -1.14 | -3.13 – 0.85 | 0.261 |
|
Group [1] × Time [D_M1_GAD_total] |
-1.92 | -4.13 – 0.30 | 0.090 |
| Random Effects | |||
| σ2 | 25.68 | ||
| Ï„00 ID | 16.28 | ||
| ICC | 0.39 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.045 / 0.415 | ||
parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.37]
## Group1 | 0.13 | [-0.18, 0.43]
## TimeD_M1_GAD_total | -0.17 | [-0.47, 0.13]
## Group1:TimeD_M1_GAD_total | -0.29 | [-0.63, 0.05]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: 10023.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8645 -0.4742 0.0781 0.5690 3.4995
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 892.4 29.87
## Residual 983.4 31.36
## Number of obs: 995, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.300 6.125 601.876 6.580 1.03e-10 ***
## Group1 -6.771 6.818 601.876 -0.993 0.321063
## TimeB_POST_mood_mean -0.020 6.272 732.226 -0.003 0.997456
## TimeC_W1_mood_mean -14.945 6.353 734.727 -2.353 0.018906 *
## TimeD_M1_mood_mean -21.094 6.582 741.330 -3.205 0.001408 **
## Group1:TimeB_POST_mood_mean 24.652 6.984 732.283 3.530 0.000442 ***
## Group1:TimeC_W1_mood_mean 9.316 7.069 734.688 1.318 0.187972
## Group1:TimeD_M1_mood_mean 14.396 7.314 741.041 1.968 0.049393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS TC_W1_ TD_M1_ G1:TB_ G1:TC_
## Group1 -0.898
## TmB_POST_m_ -0.512 0.460
## TmC_W1_md_m -0.505 0.454 0.494
## TmD_M1_md_m -0.488 0.438 0.476 0.469
## G1:TB_POST_ 0.460 -0.512 -0.898 -0.443 -0.428
## Gr1:TC_W1__ 0.454 -0.506 -0.444 -0.899 -0.422 0.494
## Gr1:TD_M1__ 0.439 -0.489 -0.429 -0.422 -0.900 0.477 0.471anova (Mood_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 974 973.6 1 259.89 0.9901 0.32065
## Time 63239 21079.7 3 737.76 21.4363 2.546e-13 ***
## Group:Time 12731 4243.6 3 737.76 4.3154 0.00499 **
## ---
## 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 [1] | -6.77 | -20.15 – 6.61 | 0.321 |
| Time [B_POST_mood_mean] | -0.02 | -12.33 – 12.29 | 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 [1] × Time [B_POST_mood_mean] |
24.65 | 10.95 – 38.36 | <0.001 |
|
Group [1] × Time [C_W1_mood_mean] |
9.32 | -4.56 – 23.19 | 0.188 |
|
Group [1] × Time [D_M1_mood_mean] |
14.40 | 0.04 – 28.75 | 0.049 |
| Random Effects | |||
| σ2 | 983.36 | ||
| Ï„00 ID | 892.40 | ||
| ICC | 0.48 | ||
| N ID | 259 | ||
| Observations | 995 | ||
| Marginal R2 / Conditional R2 | 0.075 / 0.515 | ||
parameters::standardise_parameters(Mood_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------
## (Intercept) | 0.10 | [-0.17, 0.36]
## Group1 | -0.15 | [-0.45, 0.15]
## 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]
## Group1:TimeB_POST_mood_mean | 0.55 | [ 0.24, 0.85]
## Group1:TimeC_W1_mood_mean | 0.21 | [-0.10, 0.52]
## Group1:TimeD_M1_mood_mean | 0.32 | [ 0.00, 0.64]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: 5093.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2507 -0.3943 0.0313 0.4499 3.4398
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1028.6 32.07
## Residual 520.4 22.81
## Number of obs: 517, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.300 5.566 356.438 7.241 2.77e-12 ***
## Group1 -6.771 6.196 356.438 -1.093 0.275
## TimeB_POST_mood_mean -0.020 4.562 256.161 -0.004 0.997
## Group1:TimeB_POST_mood_mean 24.582 5.081 256.227 4.838 2.27e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TB_POS
## Group1 -0.898
## TmB_POST_m_ -0.410 0.368
## G1:TB_POST_ 0.368 -0.410 -0.898anova (Mood_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 496.3 496.3 1 256.89 0.9537 0.3297
## Time 12141.7 12141.7 1 256.23 23.3309 2.349e-06 ***
## Group:Time 12181.3 12181.3 1 256.23 23.4070 2.266e-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 | 29.37 – 51.23 | <0.001 |
| Group [1] | -6.77 | -18.94 – 5.40 | 0.275 |
| Time [B_POST_mood_mean] | -0.02 | -8.98 – 8.94 | 0.997 |
|
Group [1] × Time [B_POST_mood_mean] |
24.58 | 14.60 – 34.56 | <0.001 |
| Random Effects | |||
| σ2 | 520.41 | ||
| Ï„00 ID | 1028.56 | ||
| ICC | 0.66 | ||
| N ID | 259 | ||
| Observations | 517 | ||
| Marginal R2 / Conditional R2 | 0.076 / 0.689 | ||
parameters::standardise_parameters(Mood_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------
## (Intercept) | -0.11 | [-0.38, 0.16]
## Group1 | -0.17 | [-0.46, 0.13]
## TimeB_POST_mood_mean | -4.90e-04 | [-0.22, 0.22]
## Group1:TimeB_POST_mood_mean | 0.60 | [ 0.36, 0.85]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: 5233.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1944 -0.4496 0.1002 0.6008 2.4990
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 872.5 29.54
## Residual 1113.7 33.37
## Number of obs: 509, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.300 6.303 425.716 6.394 4.25e-10 ***
## Group1 -6.771 7.016 425.716 -0.965 0.3350
## TimeC_W1_mood_mean -14.705 6.774 254.607 -2.171 0.0309 *
## Group1:TimeC_W1_mood_mean 8.821 7.537 254.475 1.170 0.2429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TmC_W1_md_m -0.522 0.469
## Gr1:TC_W1__ 0.469 -0.522 -0.899anova (Mood_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 173.2 173.2 1 258.56 0.1556 0.693601
## Time 8310.1 8310.1 1 254.47 7.4620 0.006743 **
## Group:Time 1525.5 1525.5 1 254.47 1.3698 0.242943
## ---
## 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 [1] | -6.77 | -20.56 – 7.01 | 0.335 |
| Time [C_W1_mood_mean] | -14.70 | -28.01 – -1.40 | 0.030 |
|
Group [1] × Time [C_W1_mood_mean] |
8.82 | -5.99 – 23.63 | 0.242 |
| Random Effects | |||
| σ2 | 1113.66 | ||
| Ï„00 ID | 872.53 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 509 | ||
| Marginal R2 / Conditional R2 | 0.009 / 0.444 | ||
parameters::standardise_parameters(Mood_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------
## (Intercept) | 0.20 | [-0.07, 0.48]
## Group1 | -0.15 | [-0.46, 0.16]
## TimeC_W1_mood_mean | -0.33 | [-0.63, -0.03]
## Group1:TimeC_W1_mood_mean | 0.20 | [-0.13, 0.53]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: 5024.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.87877 -0.52317 0.06649 0.59943 2.47143
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 901 30.02
## Residual 1152 33.94
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.300 6.408 412.526 6.289 8.16e-10 ***
## Group1 -6.771 7.134 412.526 -0.949 0.34307
## TimeD_M1_mood_mean -20.878 7.175 247.462 -2.910 0.00395 **
## Group1:TimeD_M1_mood_mean 14.271 7.971 246.946 1.790 0.07462 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TmD_M1_md_m -0.501 0.450
## Gr1:TD_M1__ 0.451 -0.502 -0.900anova (Mood_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.0 4.0 1 262.31 0.0035 0.9530417
## Time 13698.0 13698.0 1 246.95 11.8886 0.0006638 ***
## Group:Time 3693.2 3693.2 1 246.95 3.2054 0.0746197 .
## ---
## 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 | 27.71 – 52.89 | <0.001 |
| Group [1] | -6.77 | -20.79 – 7.25 | 0.343 |
| Time [D_M1_mood_mean] | -20.88 | -34.98 – -6.78 | 0.004 |
|
Group [1] × Time [D_M1_mood_mean] |
14.27 | -1.39 – 29.93 | 0.074 |
| Random Effects | |||
| σ2 | 1152.19 | ||
| Ï„00 ID | 901.00 | ||
| ICC | 0.44 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.014 / 0.447 | ||
parameters::standardise_parameters(Mood_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------
## (Intercept) | 0.22 | [-0.06, 0.49]
## Group1 | -0.15 | [-0.46, 0.16]
## TimeD_M1_mood_mean | -0.46 | [-0.77, -0.15]
## Group1:TimeD_M1_mood_mean | 0.31 | [-0.03, 0.66]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 == 1) #NOTE: this "intervention group" is both the mindset intervention and psychoeducation
ECs_group <- changeinvariables %>%
filter(Group == 0)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 19 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) -0.024 0.125 -0.192 0.848 -0.024 -0.010
## IUS_B1W_change ~
## Group (a1) -0.359 0.159 -2.253 0.024 -0.359 -0.142
## PHQ_B1W_change ~
## IUS_B1W_c (b1) 0.408 0.085 4.799 0.000 0.408 0.408
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.044 0.226 0.193 0.847 0.044 0.044
## .IUS_B1W_change 0.649 0.296 2.190 0.029 0.649 0.650
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.829 0.092 8.997 0.000 0.829 0.833
## .IUS_B1W_change 0.976 0.158 6.184 0.000 0.976 0.980
##
## R-Square:
## Estimate
## PHQ_B1W_change 0.167
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.146 0.067 -2.191 0.028 -0.146 -0.058
## direct -0.024 0.125 -0.192 0.848 -0.024 -0.010
## total -0.170 0.147 -1.156 0.248 -0.170 -0.067Intervention 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 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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) 0.109 0.009 12.244 0.000 0.109 0.645
## IUS_B1W_change ~
## A_PRE_PHQ (a1) -0.003 0.010 -0.262 0.793 -0.003 -0.015
## C_W1_PHQ_total ~
## IUS_B1W_c (b1) 0.286 0.076 3.757 0.000 0.286 0.286
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total -1.966 0.149 -13.198 0.000 -1.966 -1.970
## .IUS_B1W_change 0.047 0.195 0.241 0.809 0.047 0.047
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_PHQ_total 0.505 0.056 9.015 0.000 0.505 0.508
## .IUS_B1W_change 0.995 0.159 6.245 0.000 0.995 1.000
##
## R-Square:
## Estimate
## C_W1_PHQ_total 0.492
## IUS_B1W_change 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.003 -0.259 0.796 -0.001 -0.004
## direct 0.109 0.009 12.244 0.000 0.109 0.645
## total 0.108 0.009 11.731 0.000 0.108 0.641EC 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 19 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) 0.023 0.118 0.192 0.848 0.023 0.009
## IUS_B1M_change ~
## Group (a1) -0.318 0.155 -2.045 0.041 -0.318 -0.126
## PHQ_B1M_change ~
## IUS_B1M_c (b1) 0.654 0.052 12.539 0.000 0.654 0.654
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change -0.041 0.217 -0.189 0.850 -0.041 -0.041
## .IUS_B1M_change 0.574 0.287 1.999 0.046 0.574 0.575
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.572 0.063 9.015 0.000 0.572 0.574
## .IUS_B1M_change 0.980 0.122 8.054 0.000 0.980 0.984
##
## R-Square:
## Estimate
## PHQ_B1M_change 0.426
## IUS_B1M_change 0.016
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.208 0.101 -2.056 0.040 -0.208 -0.082
## direct 0.023 0.118 0.192 0.848 0.023 0.009
## total -0.185 0.167 -1.107 0.268 -0.185 -0.073Intervention 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 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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) 0.073 0.008 9.542 0.000 0.073 0.435
## IUS_B1M_change ~
## A_PRE_PHQ (a1) -0.017 0.011 -1.473 0.141 -0.017 -0.098
## D_M1_PHQ_total ~
## IUS_B1M_c (b1) 0.594 0.054 10.983 0.000 0.594 0.594
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total -1.324 0.140 -9.460 0.000 -1.324 -1.328
## .IUS_B1M_change 0.298 0.198 1.503 0.133 0.298 0.299
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_PHQ_total 0.507 0.055 9.186 0.000 0.507 0.509
## .IUS_B1M_change 0.986 0.130 7.569 0.000 0.986 0.990
##
## R-Square:
## Estimate
## D_M1_PHQ_total 0.491
## IUS_B1M_change 0.010
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.010 0.007 -1.410 0.159 -0.010 -0.058
## direct 0.073 0.008 9.542 0.000 0.073 0.435
## total 0.064 0.010 6.197 0.000 0.064 0.377EC 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 19 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) -0.038 0.111 -0.340 0.734 -0.038 -0.015
## IUS_B1W_change ~
## Group (a1) -0.359 0.159 -2.253 0.024 -0.359 -0.142
## GAD_B1W_change ~
## IUS_B1W_c (b1) 0.448 0.080 5.578 0.000 0.448 0.448
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.068 0.191 0.355 0.722 0.068 0.068
## .IUS_B1W_change 0.649 0.296 2.190 0.029 0.649 0.650
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.794 0.107 7.407 0.000 0.794 0.797
## .IUS_B1W_change 0.976 0.158 6.184 0.000 0.976 0.980
##
## R-Square:
## Estimate
## GAD_B1W_change 0.203
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.161 0.076 -2.128 0.033 -0.161 -0.064
## direct -0.038 0.111 -0.340 0.734 -0.038 -0.015
## total -0.198 0.137 -1.443 0.149 -0.198 -0.078Intervention 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 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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) 0.116 0.009 13.206 0.000 0.116 0.651
## IUS_B1W_change ~
## A_PRE_GAD (a1) -0.009 0.011 -0.784 0.433 -0.009 -0.048
## C_W1_GAD_total ~
## IUS_B1W_c (b1) 0.311 0.072 4.327 0.000 0.311 0.311
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total -1.835 0.132 -13.869 0.000 -1.835 -1.840
## .IUS_B1W_change 0.135 0.189 0.717 0.474 0.135 0.136
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_GAD_total 0.496 0.067 7.407 0.000 0.496 0.498
## .IUS_B1W_change 0.993 0.160 6.224 0.000 0.993 0.998
##
## R-Square:
## Estimate
## C_W1_GAD_total 0.502
## IUS_B1W_change 0.002
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.003 0.004 -0.740 0.460 -0.003 -0.015
## direct 0.116 0.009 13.206 0.000 0.116 0.651
## total 0.113 0.009 12.445 0.000 0.113 0.636EC 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 18 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) -0.053 0.122 -0.432 0.666 -0.053 -0.021
## IUS_B1M_change ~
## Group (a1) -0.318 0.155 -2.045 0.041 -0.318 -0.126
## GAD_B1M_change ~
## IUS_B1M_c (b1) 0.673 0.050 13.549 0.000 0.673 0.673
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.096 0.229 0.417 0.677 0.096 0.096
## .IUS_B1M_change 0.574 0.287 1.999 0.046 0.574 0.575
##
## 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 0.543
## .IUS_B1M_change 0.980 0.122 8.054 0.000 0.980 0.984
##
## R-Square:
## Estimate
## GAD_B1M_change 0.457
## IUS_B1M_change 0.016
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.214 0.105 -2.027 0.043 -0.214 -0.085
## direct -0.053 0.122 -0.432 0.666 -0.053 -0.021
## total -0.267 0.167 -1.596 0.110 -0.267 -0.105Intervention 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 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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) 0.085 0.008 11.226 0.000 0.085 0.480
## IUS_B1M_change ~
## A_PRE_GAD (a1) -0.016 0.012 -1.349 0.177 -0.016 -0.091
## D_M1_GAD_total ~
## IUS_B1M_c (b1) 0.626 0.051 12.215 0.000 0.626 0.626
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total -1.352 0.117 -11.535 0.000 -1.352 -1.355
## .IUS_B1M_change 0.256 0.185 1.389 0.165 0.256 0.257
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_GAD_total 0.431 0.051 8.449 0.000 0.431 0.433
## .IUS_B1M_change 0.987 0.130 7.599 0.000 0.987 0.992
##
## R-Square:
## Estimate
## D_M1_GAD_total 0.567
## IUS_B1M_change 0.008
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.010 0.008 -1.298 0.194 -0.010 -0.057
## direct 0.085 0.008 11.226 0.000 0.085 0.480
## total 0.075 0.011 6.796 0.000 0.075 0.423EC 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 20 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) 0.573 0.115 5.002 0.000 0.573 0.226
## IUS_BP_change ~
## Group (a1) -0.646 0.119 -5.446 0.000 -0.646 -0.255
## Mood_BP_change ~
## IUS_BP_ch (b1) -0.256 0.072 -3.533 0.000 -0.256 -0.255
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change -1.030 0.198 -5.210 0.000 -1.030 -1.028
## .IUS_BP_change 1.167 0.203 5.746 0.000 1.167 1.169
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_BP_change 0.858 0.145 5.905 0.000 0.858 0.855
## .IUS_BP_change 0.931 0.148 6.308 0.000 0.931 0.935
##
## R-Square:
## Estimate
## Mood_BP_change 0.145
## IUS_BP_change 0.065
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.165 0.054 3.084 0.002 0.165 0.065
## direct 0.573 0.115 5.002 0.000 0.573 0.226
## total 0.738 0.112 6.570 0.000 0.738 0.291Intervention 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 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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) 0.015 0.002 8.236 0.000 0.015 0.647
## IUS_BP_change ~
## A_PRE_md_ (a1) 0.002 0.001 1.121 0.262 0.002 0.072
## B_POST_mood_mean ~
## IUS_BP_ch (b1) -0.211 0.060 -3.533 0.000 -0.211 -0.211
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn -0.494 0.098 -5.041 0.000 -0.494 -0.493
## .IUS_BP_change -0.056 0.080 -0.696 0.487 -0.056 -0.056
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .B_POST_mood_mn 0.560 0.077 7.247 0.000 0.560 0.557
## .IUS_BP_change 0.990 0.173 5.722 0.000 0.990 0.995
##
## R-Square:
## Estimate
## B_POST_mood_mn 0.443
## IUS_BP_change 0.005
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -1.021 0.307 -0.000 -0.015
## direct 0.015 0.002 8.236 0.000 0.015 0.647
## total 0.015 0.002 7.970 0.000 0.015 0.631EC 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 20 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) 0.092 0.152 0.602 0.547 0.092 0.036
## IUS_B1W_change ~
## Group (a1) -0.359 0.159 -2.253 0.024 -0.359 -0.142
## Mood_B1W_change ~
## IUS_B1W_c (b1) -0.206 0.089 -2.311 0.021 -0.206 -0.205
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang -0.144 0.279 -0.515 0.607 -0.144 -0.143
## .IUS_B1W_change 0.649 0.296 2.190 0.029 0.649 0.650
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1W_chang 0.965 0.121 7.994 0.000 0.965 0.955
## .IUS_B1W_change 0.976 0.158 6.184 0.000 0.976 0.980
##
## R-Square:
## Estimate
## Mood_B1W_chang 0.045
## IUS_B1W_change 0.020
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.074 0.046 1.604 0.109 0.074 0.029
## direct 0.092 0.152 0.602 0.547 0.092 0.036
## total 0.166 0.151 1.100 0.271 0.166 0.065Intervention 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 209
## 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 6.389 0.000 0.010 0.453
## IUS_B1W_change ~
## A_PRE_md_ (a1) 0.002 0.002 0.999 0.318 0.002 0.070
## C_W1_mood_mean ~
## IUS_B1W_c (b1) -0.154 0.080 -1.929 0.054 -0.154 -0.153
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean -0.339 0.087 -3.873 0.000 -0.339 -0.339
## .IUS_B1W_change -0.054 0.087 -0.622 0.534 -0.054 -0.054
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_W1_mood_mean 0.780 0.099 7.867 0.000 0.780 0.781
## .IUS_B1W_change 0.990 0.159 6.244 0.000 0.990 0.995
##
## R-Square:
## Estimate
## C_W1_mood_mean 0.219
## IUS_B1W_change 0.005
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.000 0.000 -0.877 0.380 -0.000 -0.011
## direct 0.010 0.002 6.389 0.000 0.010 0.453
## total 0.010 0.002 5.977 0.000 0.010 0.442EC 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 19 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) 0.143 0.151 0.950 0.342 0.143 0.054
## IUS_B1M_change ~
## Group (a1) -0.318 0.155 -2.045 0.041 -0.318 -0.126
## Mood_B1M_change ~
## IUS_B1M_c (b1) -0.417 0.121 -3.452 0.001 -0.417 -0.396
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang -0.144 0.284 -0.508 0.612 -0.144 -0.137
## .IUS_B1M_change 0.574 0.287 1.999 0.046 0.574 0.575
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Mood_B1M_chang 0.924 0.103 8.933 0.000 0.924 0.835
## .IUS_B1M_change 0.980 0.122 8.054 0.000 0.980 0.984
##
## R-Square:
## Estimate
## Mood_B1M_chang 0.165
## IUS_B1M_change 0.016
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 0.133 0.075 1.761 0.078 0.133 0.050
## direct 0.143 0.151 0.950 0.342 0.143 0.054
## total 0.276 0.157 1.761 0.078 0.276 0.103Intervention 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 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 209
## 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 6.474 0.000 0.010 0.426
## IUS_B1M_change ~
## A_PRE_md_ (a1) 0.002 0.001 1.528 0.127 0.002 0.093
## D_M1_mood_mean ~
## IUS_B1M_c (b1) -0.320 0.098 -3.273 0.001 -0.320 -0.312
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean -0.250 0.088 -2.846 0.004 -0.250 -0.244
## .IUS_B1M_change -0.072 0.086 -0.840 0.401 -0.072 -0.072
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .D_M1_mood_mean 0.779 0.084 9.247 0.000 0.779 0.746
## .IUS_B1M_change 0.987 0.134 7.379 0.000 0.987 0.991
##
## R-Square:
## Estimate
## D_M1_mood_mean 0.254
## IUS_B1M_change 0.009
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.001 0.001 -1.322 0.186 -0.001 -0.029
## direct 0.010 0.002 6.474 0.000 0.010 0.426
## total 0.009 0.002 5.591 0.000 0.009 0.397EC 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
## -20.3457 -2.1977 0.6543 3.0660 12.6543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.9985 1.6374 -1.831 0.0682 .
## Group1 0.9000 1.8501 0.486 0.6271
## A_PRE_GM 0.8170 0.5335 1.531 0.1269
## Group1:A_PRE_GM -0.6689 0.5929 -1.128 0.2603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.217 on 255 degrees of freedom
## Multiple R-squared: 0.01487, Adjusted R-squared: 0.003281
## F-statistic: 1.283 on 3 and 255 DF, p-value: 0.2807anova(moderation_GM_PHQ_1W)## Analysis of Variance Table
##
## Response: PHQ_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 32.0 32.011 1.1762 0.2792
## A_PRE_GM 1 38.1 38.104 1.4001 0.2378
## Group:A_PRE_GM 1 34.6 34.642 1.2729 0.2603
## Residuals 255 6939.9 27.215# 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
## -24.079 -3.473 1.571 4.658 23.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.68312 2.52064 -0.668 0.505
## Group1 -1.71186 2.84809 -0.601 0.548
## A_PRE_GM -0.18134 0.82127 -0.221 0.825
## Group1:A_PRE_GM 0.09361 0.91268 0.103 0.918
##
## Residual standard error: 8.031 on 255 degrees of freedom
## Multiple R-squared: 0.005725, Adjusted R-squared: -0.005973
## F-statistic: 0.4894 on 3 and 255 DF, p-value: 0.6899anova(moderation_GM_PHQ_1M)## Analysis of Variance Table
##
## Response: PHQ_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 88.4 88.411 1.3708 0.2428
## A_PRE_GM 1 5.6 5.598 0.0868 0.7685
## Group:A_PRE_GM 1 0.7 0.678 0.0105 0.9184
## Residuals 255 16446.0 64.494E1: 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
## -19.1628 -1.9618 0.7678 2.4402 15.8372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1403 1.5540 -1.377 0.170
## Group1 1.5041 1.7559 0.857 0.392
## A_PRE_GM 0.6863 0.5063 1.355 0.176
## Group1:A_PRE_GM -0.8873 0.5627 -1.577 0.116
##
## Residual standard error: 4.951 on 255 degrees of freedom
## Multiple R-squared: 0.01583, Adjusted R-squared: 0.004251
## F-statistic: 1.367 on 3 and 255 DF, p-value: 0.2533anova(moderation_GM_GAD_1W)## Analysis of Variance Table
##
## Response: GAD_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 39.1 39.068 1.5938 0.2079
## A_PRE_GM 1 0.5 0.520 0.0212 0.8843
## Group:A_PRE_GM 1 60.9 60.950 2.4865 0.1161
## Residuals 255 6250.7 24.513# 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
## -23.921 -2.607 1.362 4.049 19.736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7183 2.2535 -0.762 0.446
## Group1 -0.3008 2.5463 -0.118 0.906
## A_PRE_GM 0.2110 0.7342 0.287 0.774
## Group1:A_PRE_GM -0.5544 0.8160 -0.679 0.497
##
## Residual standard error: 7.18 on 255 degrees of freedom
## Multiple R-squared: 0.01503, Adjusted R-squared: 0.003441
## F-statistic: 1.297 on 3 and 255 DF, p-value: 0.276anova(moderation_GM_GAD_1M)## Analysis of Variance Table
##
## Response: GAD_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 148.3 148.337 2.8776 0.09104 .
## A_PRE_GM 1 28.4 28.434 0.5516 0.45835
## Group:A_PRE_GM 1 23.8 23.799 0.4617 0.49746
## Residuals 255 13145.1 51.549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E1: Growth mindsets moderating the association between group and mood changes across time
# post
moderation_GM_mood_BP <- lm(Mood_BP_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_mood_BP)##
## Call:
## lm(formula = Mood_BP_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.123 -20.121 -2.878 15.227 177.386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.380 10.142 -0.826 0.40946
## Group1 36.039 11.461 3.144 0.00186 **
## A_PRE_GM 3.051 3.305 0.923 0.35674
## Group1:A_PRE_GM -4.060 3.672 -1.106 0.26997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.31 on 254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.08851, Adjusted R-squared: 0.07774
## F-statistic: 8.221 on 3 and 254 DF, p-value: 3.05e-05anova(moderation_GM_mood_BP)## Analysis of Variance Table
##
## Response: Mood_BP_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 24450 24450.0 23.4149 2.267e-06 ***
## A_PRE_GM 1 28 28.1 0.0269 0.8699
## Group:A_PRE_GM 1 1276 1276.3 1.2222 0.2700
## Residuals 254 265228 1044.2
## ---
## 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
## -185.327 -23.910 2.057 24.865 185.406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.2285 15.0280 -1.013 0.312
## Group1 15.9877 17.0154 0.940 0.348
## A_PRE_GM 0.1816 4.8651 0.037 0.970
## Group1:A_PRE_GM -2.4147 5.4165 -0.446 0.656
##
## Residual standard error: 47.42 on 246 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.008797, Adjusted R-squared: -0.00329
## F-statistic: 0.7278 on 3 and 246 DF, p-value: 0.5363anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 2929 2928.52 1.3024 0.2549
## A_PRE_GM 1 1534 1533.99 0.6822 0.4096
## Group:A_PRE_GM 1 447 446.87 0.1987 0.6561
## Residuals 246 553142 2248.55# 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
## -148.133 -30.369 4.174 28.895 179.310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.764 16.144 -1.844 0.0665 .
## Group1 20.169 18.262 1.104 0.2706
## A_PRE_GM 3.054 5.273 0.579 0.5630
## Group1:A_PRE_GM -1.997 5.875 -0.340 0.7342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.52 on 224 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.01689, Adjusted R-squared: 0.00372
## F-statistic: 1.283 on 3 and 224 DF, p-value: 0.2812anova(moderation_GM_mood_1W)## Analysis of Variance Table
##
## Response: Mood_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 1 7875 7875.2 3.3454 0.06872 .
## A_PRE_GM 1 910 910.0 0.3866 0.53474
## Group:A_PRE_GM 1 272 272.0 0.1156 0.73421
## Residuals 224 527302 2354.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1E2: 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 ' ' 1# 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: 4589.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5697 -0.3995 0.0428 0.4986 2.2700
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.499 3.082
## Residual 15.304 3.912
## Number of obs: 777, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.8600 0.7043 596.1394 21.099 <2e-16 ***
## Group1 0.2692 0.7841 596.1394 0.343 0.7315
## TimeC_W1_FI_total -0.4800 0.7824 514.0000 -0.613 0.5398
## TimeD_M1_FI_total -1.9000 0.7824 514.0000 -2.428 0.0155 *
## Group1:TimeC_W1_FI_total -0.4674 0.8710 514.0000 -0.537 0.5918
## Group1:TimeD_M1_FI_total -0.5402 0.8710 514.0000 -0.620 0.5354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_ TD_M1_ G1:TC_
## Group1 -0.898
## TmC_W1_FI_t -0.555 0.499
## TmD_M1_FI_t -0.555 0.499 0.500
## G1:TC_W1_FI 0.499 -0.555 -0.898 -0.449
## G1:TD_M1_FI 0.499 -0.555 -0.449 -0.898 0.500anova (FI_MEM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.19 0.188 1 257 0.0123 0.9118
## Time 394.86 197.428 2 514 12.9001 3.417e-06 ***
## Group:Time 6.93 3.467 2 514 0.2265 0.7974
## ---
## 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 [1] | 0.27 | -1.27 – 1.81 | 0.731 |
| 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 [1] × Time [C_W1_FI_total] |
-0.47 | -2.18 – 1.24 | 0.592 |
|
Group [1] × Time [D_M1_FI_total] |
-0.54 | -2.25 – 1.17 | 0.535 |
| Random Effects | |||
| σ2 | 15.30 | ||
| Ï„00 ID | 9.50 | ||
| ICC | 0.38 | ||
| N ID | 259 | ||
| Observations | 777 | ||
| Marginal R2 / Conditional R2 | 0.037 / 0.406 | ||
parameters::standardise_parameters(FI_MEM)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------
## (Intercept) | 0.17 | [-0.11, 0.44]
## Group1 | 0.05 | [-0.25, 0.36]
## TimeC_W1_FI_total | -0.09 | [-0.40, 0.21]
## TimeD_M1_FI_total | -0.38 | [-0.68, -0.07]
## Group1:TimeC_W1_FI_total | -0.09 | [-0.43, 0.25]
## Group1:TimeD_M1_FI_total | -0.11 | [-0.44, 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: 2920.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6846 -0.4564 -0.0069 0.5112 2.7831
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.485 3.08
## Residual 9.613 3.10
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.8600 0.6180 412.2951 24.044 <2e-16 ***
## Group1 0.2692 0.6880 412.2951 0.391 0.696
## TimeC_W1_FI_total -0.4800 0.6201 257.0000 -0.774 0.440
## Group1:TimeC_W1_FI_total -0.4674 0.6903 257.0000 -0.677 0.499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TC_W1_
## Group1 -0.898
## TmC_W1_FI_t -0.502 0.451
## G1:TC_W1_FI 0.451 -0.502 -0.898anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.034 0.034 1 257 0.0036 0.95248
## Time 41.102 41.102 1 257 4.2758 0.03966 *
## Group:Time 4.407 4.407 1 257 0.4584 0.49897
## ---
## 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 | 13.65 – 16.07 | <0.001 |
| Group [1] | 0.27 | -1.08 – 1.62 | 0.696 |
| Time [C_W1_FI_total] | -0.48 | -1.70 – 0.74 | 0.439 |
|
Group [1] × Time [C_W1_FI_total] |
-0.47 | -1.82 – 0.89 | 0.499 |
| Random Effects | |||
| σ2 | 9.61 | ||
| Ï„00 ID | 9.49 | ||
| ICC | 0.50 | ||
| N ID | 259 | ||
| Observations | 518 | ||
| Marginal R2 / Conditional R2 | 0.010 / 0.502 | ||
parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------
## (Intercept) | 0.05 | [-0.23, 0.33]
## Group1 | 0.06 | [-0.25, 0.37]
## TimeC_W1_FI_total | -0.11 | [-0.39, 0.17]
## Group1:TimeC_W1_FI_total | -0.11 | [-0.42, 0.20]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: 3118.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73092 -0.42014 0.07966 0.52071 2.06714
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.557 3.091
## Residual 16.619 4.077
## Number of obs: 518, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.8600 0.7235 453.5415 20.538 <2e-16 ***
## Group1 0.2692 0.8055 453.5415 0.334 0.7384
## TimeD_M1_FI_total -1.9000 0.8153 257.0000 -2.330 0.0206 *
## Group1:TimeD_M1_FI_total -0.5402 0.9076 257.0000 -0.595 0.5523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Group1 TD_M1_
## Group1 -0.898
## TmD_M1_FI_t -0.563 0.506
## G1:TD_M1_FI 0.506 -0.563 -0.898anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.00 0.00 1 257 0.0000 0.9989
## Time 380.02 380.02 1 257 22.8669 2.927e-06 ***
## Group:Time 5.89 5.89 1 257 0.3542 0.5523
## ---
## 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 [1] | 0.27 | -1.31 – 1.85 | 0.738 |
| Time [D_M1_FI_total] | -1.90 | -3.50 – -0.30 | 0.020 |
|
Group [1] × Time [D_M1_FI_total] |
-0.54 | -2.32 – 1.24 | 0.552 |
| Random Effects | |||
| σ2 | 16.62 | ||
| Ï„00 ID | 9.56 | ||
| 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]
## Group1 | 0.05 | [-0.25, 0.35]
## TimeD_M1_FI_total | -0.36 | [-0.67, -0.06]
## Group1:TimeD_M1_FI_total | -0.10 | [-0.44, 0.24]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)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_GM 1 745.6 745.58 9.8203 0.001926 **
## Residuals 257 19512.1 75.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1