library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(janitor) ##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(haven)
library(naniar)
library(ggpubr)
library(report)
library(ggplot2)
library(reshape2)##
## Attaching package: 'reshape2'
##
## The following object is masked from 'package:tidyr':
##
## smithslibrary(lme4)## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpacklibrary(sjPlot)
library(parameters)
library(mediation)## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: mvtnorm
## Loading required package: sandwich
## mediation: Causal Mediation Analysis
## Version: 4.5.0library(lavaan)## This is lavaan 0.6-15
## lavaan is FREE software! Please report any bugs.library(lmerTest)##
## Attaching package: 'lmerTest'
##
## The following object is masked from 'package:lme4':
##
## lmer
##
## The following object is masked from 'package:stats':
##
## steplibrary(modEvA)
library(rsconnect)
library(effectsize)##
## Attaching package: 'effectsize'
##
## The following object is masked from 'package:mvtnorm':
##
## standardizelibrary(emmeans)
library(performance)
library(tinytex)Full_data_all <- read_csv("Full_data_all.csv")## Rows: 259 Columns: 208
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Group
## dbl (207): ID, B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5, B_IUS_6, B_IUS_7...
##
## 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.BT_full_raw <- read_csv("Full_BT_data.csv") %>%
dplyr::select("ID", "A_PRE_samples", "B_POST_samples") # numbers in the PRE_samples and POST_samples columns are the number of times participants sampled across each set of 10 trials (maximum being 300 - one ppt has 308 due to a glitch)## New names:
## Rows: 259 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (4): ...1, ID, A_PRE_samples, B_POST_samples
## 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`#Depression
PRE_IUS_PHQ_lm <- lm(A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
summary(PRE_IUS_PHQ_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_PHQ_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.9576 -4.5129 0.0901 4.9155 22.1617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.4890 0.9776 36.303 < 2e-16 ***
## A_PRE_PHQ_total 0.6746 0.0841 8.021 3.72e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.94 on 257 degrees of freedom
## Multiple R-squared: 0.2002, Adjusted R-squared: 0.1971
## F-statistic: 64.34 on 1 and 257 DF, p-value: 3.716e-14cor.test(Full_data_all$A_PRE_IUS_total, Full_data_all$A_PRE_PHQ_total, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: Full_data_all$A_PRE_IUS_total and Full_data_all$A_PRE_PHQ_total
## t = 8.0215, df = 257, p-value = 3.716e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3443686 0.5399152
## sample estimates:
## cor
## 0.4474747#Anxiety
PRE_IUS_GAD_lm <- lm(A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
summary(PRE_IUS_GAD_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_GAD_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.3228 -4.8228 0.7116 4.1772 20.6429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.04669 0.88936 39.41 <2e-16 ***
## A_PRE_GAD_total 0.82761 0.08639 9.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.621 on 257 degrees of freedom
## Multiple R-squared: 0.2631, Adjusted R-squared: 0.2603
## F-statistic: 91.78 on 1 and 257 DF, p-value: < 2.2e-16cor.test(Full_data_all$A_PRE_IUS_total, Full_data_all$A_PRE_GAD_total, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: Full_data_all$A_PRE_IUS_total and Full_data_all$A_PRE_GAD_total
## t = 9.5802, df = 257, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4171735 0.5975068
## sample estimates:
## cor
## 0.5129779Set up
# Adding in groups + excluding participants who only sampled (never made a choice = did not understand task)
BT_PRE_POST <- merge(BT_full_raw,Full_data_all,
by=c("ID"),
all = TRUE) %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(ID != "8892522", ID != "8892570", ID != "8892628", ID != "8892668", ID != "8892681", ID != "8892779", ID != "8892794", ID != "8893157", ID != "8893186", ID != "8892873", ID != "9113535", ID != "9113549", ID != "9113550") # excluding those not making a choicePRE_IUS_BT_lm <- lm(A_PRE_IUS_total ~ A_PRE_samples, data = BT_PRE_POST)
summary(PRE_IUS_BT_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_samples, data = BT_PRE_POST)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.813 -5.889 1.036 6.280 17.187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.81292 0.61535 69.575 < 2e-16 ***
## A_PRE_samples -0.04991 0.01818 -2.745 0.00651 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.754 on 244 degrees of freedom
## Multiple R-squared: 0.02995, Adjusted R-squared: 0.02598
## F-statistic: 7.534 on 1 and 244 DF, p-value: 0.006505cor.test(BT_PRE_POST$A_PRE_IUS_total, BT_PRE_POST$A_PRE_samples, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: BT_PRE_POST$A_PRE_IUS_total and BT_PRE_POST$A_PRE_samples
## t = -2.7448, df = 244, p-value = 0.006505
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.29182197 -0.04905378
## sample estimates:
## cor
## -0.1730653BT_removed_PRE <- BT_PRE_POST %>%
filter(A_PRE_samples != "0") # only excluding from PRE
PRE_IUS_BT_removed_lm <- lm(A_PRE_IUS_total ~ A_PRE_samples, data = BT_removed_PRE)
summary(PRE_IUS_BT_removed_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_samples, data = BT_removed_PRE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.606 -5.932 1.014 5.934 15.934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.14794 0.72669 59.376 < 2e-16 ***
## A_PRE_samples -0.05408 0.01801 -3.003 0.00307 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.265 on 171 degrees of freedom
## Multiple R-squared: 0.0501, Adjusted R-squared: 0.04454
## F-statistic: 9.018 on 1 and 171 DF, p-value: 0.003074cor.test(BT_removed_PRE$A_PRE_IUS_total, BT_removed_PRE$A_PRE_samples, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: BT_removed_PRE$A_PRE_IUS_total and BT_removed_PRE$A_PRE_samples
## t = -3.0031, df = 171, p-value = 0.003074
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.36096987 -0.07720195
## sample estimates:
## cor
## -0.2238241IUS_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total")
IUS_BP_long <- IUS_BP %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_BP <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long, REML = TRUE)
summary(IUS_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_BP_long
##
## REML criterion at convergence: 3592.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95161 -0.44757 0.00781 0.41532 2.86841
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 71.13 8.434
## Residual 24.28 4.927
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3814 328.7093 29.738
## GroupB_Controls 0.9483 1.6758 328.7093 0.566
## GroupC_Intervention 1.9880 1.6836 328.7093 1.181
## TimeB_POST_IUS_total -0.2800 0.9854 254.2485 -0.284
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 1.1955 254.2485 -3.001
## GroupC_Intervention:TimeB_POST_IUS_total -6.0437 1.2044 254.6121 -5.018
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.57186
## GroupC_Intervention 0.23854
## TimeB_POST_IUS_total 0.77654
## GroupB_Controls:TimeB_POST_IUS_total 0.00296 **
## GroupC_Intervention:TimeB_POST_IUS_total 9.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TB_POST_IUS -0.357 0.294 0.293
## GB_C:TB_POS 0.294 -0.357 -0.241 -0.824
## GC_I:TB_POS 0.292 -0.241 -0.356 -0.818 0.674anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 10.89 5.45 2 256.08 0.2243 0.7992
## Time 1394.73 1394.73 1 254.53 57.4503 6.459e-13 ***
## Group:Time 617.82 308.91 2 254.58 12.7243 5.407e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(IUS_MEM_BP)## # R2 for Mixed Models
##
## Conditional R2: 0.760
## Marginal R2: 0.056parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.36]
## GroupB_Controls | 0.09 | [-0.23, 0.42]
## GroupC_Intervention | 0.20 | [-0.13, 0.53]
## TimeB_POST_IUS_total | -0.03 | [-0.22, 0.17]
## GroupB_Controls:TimeB_POST_IUS_total | -0.36 | [-0.59, -0.12]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.60 | [-0.84, -0.37]Comparing intervention and psychoed groups
IUS_BP_IC <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group != "A_ECs")
IUS_BP_long_IC <- IUS_BP_IC %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_BP_IC <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long_IC, REML = TRUE)
summary(IUS_MEM_BP_IC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_BP_long_IC
##
## REML criterion at convergence: 2932.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.82123 -0.43408 0.01576 0.42315 2.74006
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 76.04 8.720
## Residual 26.59 5.156
## Number of obs: 416, groups: ID, 209
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 0.9840 266.9103 42.714
## GroupC_Intervention 1.0397 1.4016 266.9103 0.742
## TimeB_POST_IUS_total -3.8679 0.7083 205.2694 -5.461
## GroupC_Intervention:TimeB_POST_IUS_total -2.4555 1.0133 205.7308 -2.423
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupC_Intervention 0.4589
## TimeB_POST_IUS_total 1.36e-07 ***
## GroupC_Intervention:TimeB_POST_IUS_total 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpC_I TB_POS
## GrpC_Intrvn -0.702
## TB_POST_IUS -0.360 0.253
## GC_I:TB_POS 0.252 -0.358 -0.699anova (IUS_MEM_BP_IC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.55 0.55 1 207.22 0.0207 0.88583
## Time 2689.38 2689.38 1 205.73 101.1464 < 2e-16 ***
## Group:Time 156.13 156.13 1 205.73 5.8719 0.01625 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_BP_IC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.19 | [ 0.01, 0.38]
## GroupC_Intervention | 0.10 | [-0.16, 0.36]
## TimeB_POST_IUS_total | -0.37 | [-0.50, -0.24]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.24 | [-0.43, -0.04]Intervention group
IUS_I_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "C_Intervention")
IUS_I_long_p <- IUS_I_p %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_I_p <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_I_long_p, REML = TRUE)
summary(IUS_MEM_I_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_I_long_p
##
## REML criterion at convergence: 1458.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51489 -0.43453 0.03273 0.35595 2.42007
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 69.41 8.331
## Residual 33.77 5.812
## Number of obs: 204, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.0680 1.0009 139.9412 43.03 < 2e-16 ***
## TimeB_POST_IUS_total -6.3200 0.8165 100.8777 -7.74 7.84e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TB_POST_IUS -0.401anova (IUS_MEM_I_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 2023.5 2023.5 1 100.88 59.912 7.843e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_I_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.29 | [ 0.11, 0.48]
## TimeB_POST_IUS_total | -0.59 | [-0.75, -0.44]Psychoeducation group
IUS_C_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "B_Controls")
IUS_C_long_p <- IUS_C_p %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_C_p <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_C_long_p, REML = TRUE)
summary(IUS_MEM_C_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_C_long_p
##
## REML criterion at convergence: 1466.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89613 -0.42593 -0.04266 0.47538 2.98785
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 82.41 9.078
## Residual 19.72 4.441
## Number of obs: 212, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.0283 0.9816 127.1907 42.81 < 2e-16 ***
## TimeB_POST_IUS_total -3.8679 0.6100 105.0000 -6.34 5.87e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TB_POST_IUS -0.311anova (IUS_MEM_C_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 792.92 792.92 1 105 40.2 5.866e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_C_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------
## (Intercept) | 0.19 | [ 0.00, 0.38]
## TimeB_POST_IUS_total | -0.38 | [-0.49, -0.26]Non-active control group
IUS_EC_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "A_ECs")
IUS_EC_long_p <- IUS_EC_p %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_EC_p <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_EC_long_p, REML = TRUE)
summary(IUS_MEM_EC_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_EC_long_p
##
## REML criterion at convergence: 650
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7648 -0.5669 0.0155 0.4452 2.4970
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 50.40 7.10
## Residual 14.59 3.82
## Number of obs: 100, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.140 61.197 36.030 <2e-16 ***
## TimeB_POST_IUS_total -0.280 0.764 49.000 -0.366 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TB_POST_IUS -0.335anova (IUS_MEM_EC_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 1.96 1.96 1 49 0.1343 0.7156parameters::standardise_parameters(IUS_MEM_EC_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------
## (Intercept) | 0.02 | [-0.26, 0.30]
## TimeB_POST_IUS_total | -0.03 | [-0.22, 0.15]# IUS - post
m.ef_ius_p<-emmeans(IUS_MEM_BP, "Time", "Group")
eff_size(m.ef_ius_p, sigma = sigma(IUS_MEM_BP), edf = df.residual(IUS_MEM_BP))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - B_POST_IUS_total 0.0568 0.200 329 -0.337 0.45
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - B_POST_IUS_total 0.7850 0.140 329 0.510 1.06
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - B_POST_IUS_total 1.2834 0.146 329 0.996 1.57
##
## sigma used for effect sizes: 4.927
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95IUS_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total")
IUS_B1W_long <- IUS_B1W %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_B1W <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long, REML = TRUE)
summary(IUS_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1W_long
##
## REML criterion at convergence: 3541.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.05745 -0.40449 -0.00329 0.45694 2.90920
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 64.04 8.003
## Residual 24.69 4.969
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3321 334.4251 30.837
## GroupB_Controls 0.9483 1.6161 334.4251 0.587
## GroupC_Intervention 1.9880 1.6236 334.4251 1.224
## TimeC_W1_IUS_total 0.8991 1.0114 251.5502 0.889
## GroupB_Controls:TimeC_W1_IUS_total -2.7707 1.2233 251.1429 -2.265
## GroupC_Intervention:TimeC_W1_IUS_total -5.4023 1.2307 251.3348 -4.390
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5577
## GroupC_Intervention 0.2217
## TimeC_W1_IUS_total 0.3749
## GroupB_Controls:TimeC_W1_IUS_total 0.0244 *
## GroupC_Intervention:TimeC_W1_IUS_total 1.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_IUS_ -0.366 0.302 0.301
## GB_C:TC_W1_ 0.303 -0.368 -0.249 -0.827
## GC_I:TC_W1_ 0.301 -0.248 -0.367 -0.822 0.679anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.54 2.77 2 256.11 0.1122 0.8938692
## Time 372.30 372.30 1 251.08 15.0803 0.0001318 ***
## Group:Time 499.29 249.64 2 250.97 10.1120 5.976e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(IUS_MEM_B1W)## # R2 for Mixed Models
##
## Conditional R2: 0.729
## Marginal R2: 0.027parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -7.24e-03 | [-0.28, 0.27]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.55]
## TimeC_W1_IUS_total | 0.09 | [-0.11, 0.30]
## GroupB_Controls:TimeC_W1_IUS_total | -0.29 | [-0.55, -0.04]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.57 | [-0.82, -0.31]Comparing intervention and psychoed groups
IUS_B1W_IC <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group != "A_ECs")
IUS_B1W_long_IC <- IUS_B1W_IC %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_B1W_IC <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long_IC, REML = TRUE)
summary(IUS_MEM_B1W_IC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1W_long_IC
##
## REML criterion at convergence: 2902.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89876 -0.43691 0.03033 0.45405 2.74965
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 68.32 8.265
## Residual 27.60 5.253
## Number of obs: 413, groups: ID, 209
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 42.0283 0.9512 272.7572 44.184
## GroupC_Intervention 1.0397 1.3550 272.7572 0.767
## TimeC_W1_IUS_total -1.8705 0.7275 202.7497 -2.571
## GroupC_Intervention:TimeC_W1_IUS_total -2.6297 1.0387 203.0139 -2.532
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupC_Intervention 0.4436
## TimeC_W1_IUS_total 0.0109 *
## GroupC_Intervention:TimeC_W1_IUS_total 0.0121 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpC_I TC_W1_
## GrpC_Intrvn -0.702
## TmC_W1_IUS_ -0.376 0.264
## GC_I:TC_W1_ 0.263 -0.375 -0.700anova (IUS_MEM_B1W_IC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.32 1.32 1 206.62 0.048 0.82677
## Time 1038.14 1038.14 1 203.01 37.619 4.422e-09 ***
## Group:Time 176.89 176.89 1 203.01 6.410 0.01211 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_B1W_IC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.10 | [-0.09, 0.29]
## GroupC_Intervention | 0.11 | [-0.16, 0.37]
## TimeC_W1_IUS_total | -0.19 | [-0.33, -0.04]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.27 | [-0.47, -0.06]Intervention group
IUS_I_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "C_Intervention")
IUS_I_long_1w <- IUS_I_1w %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_I_1w <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_I_long_1w, REML = TRUE)
summary(IUS_MEM_I_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_I_long_1w
##
## REML criterion at convergence: 1433.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.75154 -0.41446 0.07427 0.44626 2.56493
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 61.29 7.829
## Residual 31.45 5.608
## Number of obs: 203, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.0680 0.9489 140.1867 45.386 < 2e-16 ***
## TimeC_W1_IUS_total -4.4842 0.7912 99.1655 -5.668 1.42e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmC_W1_IUS_ -0.407anova (IUS_MEM_I_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 1010.4 1010.4 1 99.166 32.123 1.424e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_I_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.21 | [ 0.02, 0.41]
## TimeC_W1_IUS_total | -0.46 | [-0.62, -0.30]Psychoeducation group
IUS_C_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "B_Controls")
IUS_C_long_1w <- IUS_C_1w %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_C_1w <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_C_long_1w, REML = TRUE)
summary(IUS_MEM_C_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_C_long_1w
##
## REML criterion at convergence: 1467.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1362 -0.4476 -0.0080 0.4750 2.3101
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 74.91 8.655
## Residual 23.94 4.893
## Number of obs: 210, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.0283 0.9657 133.0949 43.520 < 2e-16 ***
## TimeC_W1_IUS_total -1.8758 0.6778 103.7076 -2.768 0.00669 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmC_W1_IUS_ -0.345anova (IUS_MEM_C_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 183.38 183.38 1 103.71 7.6596 0.006689 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_C_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.09 | [-0.10, 0.28]
## TimeC_W1_IUS_total | -0.19 | [-0.32, -0.05]Non-active control group
IUS_EC_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "A_ECs")
IUS_EC_long_1w <- IUS_EC_1w %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_EC_1w <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_EC_long_1w, REML = TRUE)
summary(IUS_MEM_EC_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_EC_long_1w
##
## REML criterion at convergence: 624.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7506 -0.4373 -0.1144 0.3660 1.9978
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 45.92 6.776
## Residual 12.25 3.500
## Number of obs: 98, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.0800 1.0786 60.1817 38.087 <2e-16 ***
## TimeC_W1_IUS_total 0.8932 0.7129 47.6699 1.253 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmC_W1_IUS_ -0.319anova (IUS_MEM_EC_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 19.23 19.23 1 47.67 1.5701 0.2163parameters::standardise_parameters(IUS_MEM_EC_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.06 | [-0.34, 0.22]
## TimeC_W1_IUS_total | 0.12 | [-0.07, 0.30]# IUS - post
m.ef_ius_1w<-emmeans(IUS_MEM_B1W, "Time", "Group")
eff_size(m.ef_ius_1w, sigma = sigma(IUS_MEM_B1W), edf = df.residual(IUS_MEM_B1W))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - C_W1_IUS_total -0.181 0.204 334 -0.582 0.22
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - C_W1_IUS_total 0.377 0.139 334 0.103 0.65
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - C_W1_IUS_total 0.906 0.144 334 0.623 1.19
##
## sigma used for effect sizes: 4.969
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95IUS_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total")
IUS_B1M_long <- IUS_B1M %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_B1M <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1M_long, REML = TRUE)
summary(IUS_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1M_long
##
## REML criterion at convergence: 3427.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95787 -0.46559 0.01777 0.50003 2.25579
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.99 7.745
## Residual 29.75 5.454
## Number of obs: 488, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3397 343.4368 30.664
## GroupB_Controls 0.9483 1.6252 343.4368 0.583
## GroupC_Intervention 1.9880 1.6328 343.4368 1.218
## TimeD_M1_IUS_total 2.2079 1.1512 235.9671 1.918
## GroupB_Controls:TimeD_M1_IUS_total -3.9626 1.3951 235.8181 -2.840
## GroupC_Intervention:TimeD_M1_IUS_total -6.9119 1.4023 235.8887 -4.929
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5599
## GroupC_Intervention 0.2242
## TimeD_M1_IUS_total 0.0563 .
## GroupB_Controls:TimeD_M1_IUS_total 0.0049 **
## GroupC_Intervention:TimeD_M1_IUS_total 1.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_IUS_ -0.386 0.318 0.317
## GB_C:TD_M1_ 0.318 -0.386 -0.261 -0.825
## GC_I:TD_M1_ 0.317 -0.261 -0.386 -0.821 0.677anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 28.24 14.12 2 257.23 0.4747 0.622614
## Time 207.73 207.73 1 235.80 6.9828 0.008781 **
## Group:Time 736.40 368.20 2 235.76 12.3767 7.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(IUS_MEM_B1M)## # R2 for Mixed Models
##
## Conditional R2: 0.679
## Marginal R2: 0.032parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.02 | [-0.30, 0.25]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.54]
## TimeD_M1_IUS_total | 0.23 | [-0.01, 0.47]
## GroupB_Controls:TimeD_M1_IUS_total | -0.41 | [-0.70, -0.13]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.72 | [-1.01, -0.43]Intervention group
IUS_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "C_Intervention")
IUS_I_long_1m <- IUS_I_1m %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_I_1m <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_I_long_1m, REML = TRUE)
summary(IUS_MEM_I_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_I_long_1m
##
## REML criterion at convergence: 1377.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21864 -0.50213 0.02461 0.53633 2.12054
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.37 7.705
## Residual 33.76 5.811
## Number of obs: 194, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 43.0680 0.9509 140.9831 45.290 < 2e-16 ***
## TimeD_M1_IUS_total -4.7031 0.8523 94.9404 -5.518 2.96e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_IUS_ -0.404anova (IUS_MEM_I_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 1028.1 1028.1 1 94.94 30.449 2.961e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_I_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.22 | [ 0.03, 0.41]
## TimeD_M1_IUS_total | -0.47 | [-0.64, -0.30]Psychoeduction group
IUS_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "B_Controls")
IUS_C_long_1m <- IUS_C_1m %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_C_1m <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_C_long_1m, REML = TRUE)
summary(IUS_MEM_C_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_C_long_1m
##
## REML criterion at convergence: 1431.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.78127 -0.44321 0.07034 0.45684 2.03174
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 69.93 8.363
## Residual 33.21 5.763
## Number of obs: 200, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.0283 0.9864 139.0853 42.61 <2e-16 ***
## TimeD_M1_IUS_total -1.7574 0.8329 95.8821 -2.11 0.0375 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_IUS_ -0.381anova (IUS_MEM_C_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 147.85 147.85 1 95.882 4.4519 0.03747 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_C_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.07 | [-0.12, 0.26]
## TimeD_M1_IUS_total | -0.17 | [-0.34, -0.01]Non-active control group
IUS_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "A_ECs")
IUS_EC_long_1m <- IUS_EC_1m %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_MEM_EC_1m <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_EC_long_1m, REML = TRUE)
summary(IUS_MEM_EC_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Time + (1 | ID)
## Data: IUS_EC_long_1m
##
## REML criterion at convergence: 601
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1175 -0.4876 -0.1018 0.5149 1.5792
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 40.03 6.327
## Residual 13.84 3.720
## Number of obs: 94, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 41.080 1.038 61.951 39.577 < 2e-16 ***
## TimeD_M1_IUS_total 2.197 0.787 44.914 2.791 0.00768 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_IUS_ -0.339anova (IUS_MEM_EC_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 107.85 107.85 1 44.914 7.7919 0.007679 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(IUS_MEM_EC_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.14 | [-0.42, 0.13]
## TimeD_M1_IUS_total | 0.29 | [ 0.09, 0.50]# IUS - post
m.ef_ius_1m<-emmeans(IUS_MEM_B1M, "Time", "Group")
eff_size(m.ef_ius_1m, sigma = sigma(IUS_MEM_B1M), edf = df.residual(IUS_MEM_B1M))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - D_M1_IUS_total -0.405 0.212 342 -0.8209 0.0112
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - D_M1_IUS_total 0.322 0.145 342 0.0367 0.6067
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_IUS_total - D_M1_IUS_total 0.862 0.149 342 0.5685 1.1564
##
## sigma used for effect sizes: 5.454
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95BT_full <- BT_PRE_POST %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP <- BT_full %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP_long <- BT_BP %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")BT_BP_removed <- BT_BP %>%
filter(A_PRE_samples != "0") %>% # only excluding from PRE
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP_long_removed <- BT_BP_removed %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_BP_removed <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_removed, REML = TRUE)
summary(BT_MEM_BP_removed)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long_removed
##
## REML criterion at convergence: 3169.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0812 -0.2761 -0.0683 0.1403 6.8856
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 429.4 20.72
## Residual 358.4 18.93
## Number of obs: 343, groups: ID, 173
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 16.7895 4.5533 261.0959 3.687
## GroupB_Controls 9.2408 5.7157 261.0959 1.617
## GroupC_Intervention -0.1228 5.6702 261.0959 -0.022
## TimeB_POST_samples -1.1053 4.3434 168.0021 -0.254
## GroupB_Controls:TimeB_POST_samples -11.2692 5.4762 168.6705 -2.058
## GroupC_Intervention:TimeB_POST_samples -3.4718 5.4196 168.3090 -0.641
## Pr(>|t|)
## (Intercept) 0.000276 ***
## GroupB_Controls 0.107143
## GroupC_Intervention 0.982737
## TimeB_POST_samples 0.799442
## GroupB_Controls:TimeB_POST_samples 0.041143 *
## GroupC_Intervention:TimeB_POST_samples 0.522658
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.797
## GrpC_Intrvn -0.803 0.640
## TmB_POST_sm -0.477 0.380 0.383
## GB_C:TB_POS 0.378 -0.475 -0.304 -0.793
## GC_I:TB_POS 0.382 -0.304 -0.476 -0.801 0.636anova (BT_MEM_BP_removed)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 601.59 300.79 2 170.47 0.8392 0.433833
## Time 2885.84 2885.84 1 168.72 8.0513 0.005105 **
## Group:Time 1785.79 892.90 2 168.82 2.4911 0.085856 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(BT_MEM_BP_removed)## # R2 for Mixed Models
##
## Conditional R2: 0.558
## Marginal R2: 0.028parameters::standardise_parameters(BT_MEM_BP_removed)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -6.00e-03 | [-0.32, 0.31]
## GroupB_Controls | 0.33 | [-0.07, 0.72]
## GroupC_Intervention | -4.34e-03 | [-0.40, 0.39]
## TimeB_POST_samples | -0.04 | [-0.34, 0.26]
## GroupB_Controls:TimeB_POST_samples | -0.40 | [-0.78, -0.02]
## GroupC_Intervention:TimeB_POST_samples | -0.12 | [-0.50, 0.25]Comparing intervention and psychoed groups
BT_BP_IC <- BT_BP_removed %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples") %>%
filter(Group != "A_ECs")
BT_BP_long_IC <- BT_BP_IC %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_BP_IC <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_IC, REML = TRUE)
summary(BT_MEM_BP_IC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long_IC
##
## REML criterion at convergence: 2518
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4599 -0.2873 -0.0982 0.1635 6.2722
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 456.3 21.36
## Residual 455.2 21.34
## Number of obs: 267, groups: ID, 135
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 26.030 3.716 211.489 7.004
## GroupC_Intervention -9.364 5.198 211.489 -1.801
## TimeB_POST_samples -12.359 3.757 132.624 -3.289
## GroupC_Intervention:TimeB_POST_samples 7.790 5.240 132.240 1.487
## Pr(>|t|)
## (Intercept) 3.25e-11 ***
## GroupC_Intervention 0.07308 .
## TimeB_POST_samples 0.00129 **
## GroupC_Intervention:TimeB_POST_samples 0.13947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpC_I TB_POS
## GrpC_Intrvn -0.715
## TmB_POST_sm -0.494 0.353
## GC_I:TB_POS 0.354 -0.495 -0.717anova (BT_MEM_BP_IC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 667.5 667.5 1 133.77 1.4664 0.228044
## Time 4750.6 4750.6 1 132.24 10.4361 0.001559 **
## Group:Time 1006.2 1006.2 1 132.24 2.2103 0.139473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_BP_IC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.29 | [ 0.05, 0.53]
## GroupC_Intervention | -0.31 | [-0.64, 0.03]
## TimeB_POST_samples | -0.40 | [-0.65, -0.16]
## GroupC_Intervention:TimeB_POST_samples | 0.26 | [-0.08, 0.59]Intervention group
BT_I_p <- BT_BP_removed %>%
filter(Group == "C_Intervention")
BT_I_long_p <- BT_I_p %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_I_p <- lmer(BT_Score ~ Time + (1|ID), data = BT_I_long_p, REML = TRUE)
summary(BT_MEM_I_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_I_long_p
##
## REML criterion at convergence: 1140.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6981 -0.3709 -0.1205 0.2751 5.7490
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 179.3 13.39
## Residual 134.1 11.58
## Number of obs: 137, groups: ID, 69
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.667 2.131 102.084 7.82 4.99e-12 ***
## TimeB_POST_samples -4.582 1.983 67.537 -2.31 0.0239 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.460anova (BT_MEM_I_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 716.05 716.05 1 67.537 5.3379 0.02393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_I_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.12 | [-0.11, 0.36]
## TimeB_POST_samples | -0.26 | [-0.48, -0.04]Psychoeducation group
BT_C_p <- BT_BP_removed %>%
filter(Group == "B_Controls")
BT_C_long_p <- BT_C_p %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_C_p <- lmer(BT_Score ~ Time + (1|ID), data = BT_C_long_p, REML = TRUE)
summary(BT_MEM_C_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_C_long_p
##
## REML criterion at convergence: 1294.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5709 -0.3255 -0.1085 0.1256 4.7932
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 746.3 27.32
## Residual 795.1 28.20
## Number of obs: 130, groups: ID, 66
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 26.030 4.833 104.490 5.386 4.46e-07 ***
## TimeB_POST_samples -12.353 4.965 64.569 -2.488 0.0154 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.502anova (BT_MEM_C_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 4921.9 4921.9 1 64.569 6.1906 0.01543 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_C_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.15 | [-0.09, 0.39]
## TimeB_POST_samples | -0.31 | [-0.56, -0.06]Non-active control group
BT_EC_p <- BT_BP_removed %>%
filter(Group == "A_ECs")
BT_EC_long_p <- BT_EC_p %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_EC_p <- lmer(BT_Score ~ Time + (1|ID), data = BT_EC_long_p, REML = TRUE)
summary(BT_MEM_EC_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_EC_long_p
##
## REML criterion at convergence: 561.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5291 -0.3098 -0.0795 0.2461 3.1860
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 332.07 18.223
## Residual 16.05 4.006
## Number of obs: 76, groups: ID, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.790 3.027 38.745 5.547 2.25e-06 ***
## TimeB_POST_samples -1.105 0.919 37.000 -1.203 0.237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.152anova (BT_MEM_EC_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 23.21 23.21 1 37 1.4463 0.2368parameters::standardise_parameters(BT_MEM_EC_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | 0.03 | [-0.30, 0.36]
## TimeB_POST_samples | -0.06 | [-0.16, 0.04]m.ef_BT_p<-emmeans(BT_MEM_BP_removed, "Time", "Group")
eff_size(m.ef_BT_p, sigma = sigma(BT_MEM_BP_removed), edf = df.residual(BT_MEM_BP_removed))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.0584 0.229 260 -0.3934 0.510
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.6536 0.178 260 0.3031 1.004
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.2418 0.171 260 -0.0959 0.579
##
## sigma used for effect sizes: 18.93
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95BT_BP <- BT_full %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP_long <- BT_BP %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_BP <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long, REML = TRUE)
summary(BT_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long
##
## REML criterion at convergence: 4395.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9264 -0.2398 -0.0766 0.0958 8.1574
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 354 18.81
## Residual 263 16.22
## Number of obs: 488, groups: ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 13.5745 3.6233 364.5272 3.746
## GroupB_Controls 3.6055 4.3930 364.5272 0.821
## GroupC_Intervention -1.9583 4.4001 364.5272 -0.445
## TimeB_POST_samples -0.5106 3.3457 240.3829 -0.153
## GroupB_Controls:TimeB_POST_samples -7.2867 4.0668 240.9058 -1.792
## GroupC_Intervention:TimeB_POST_samples -2.4873 4.0736 240.9148 -0.611
## Pr(>|t|)
## (Intercept) 0.000208 ***
## GroupB_Controls 0.412330
## GroupC_Intervention 0.656542
## TimeB_POST_samples 0.878821
## GroupB_Controls:TimeB_POST_samples 0.074429 .
## GroupC_Intervention:TimeB_POST_samples 0.542042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.825
## GrpC_Intrvn -0.823 0.679
## TmB_POST_sm -0.462 0.381 0.380
## GB_C:TB_POS 0.380 -0.461 -0.313 -0.823
## GC_I:TB_POS 0.379 -0.313 -0.460 -0.821 0.676anova (BT_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 322.16 161.08 2 243.67 0.6124 0.54290
## Time 1532.59 1532.59 1 241.18 5.8262 0.01653 *
## Group:Time 1018.76 509.38 2 241.35 1.9364 0.14645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(BT_MEM_BP)## # R2 for Mixed Models
##
## Conditional R2: 0.580
## Marginal R2: 0.015parameters::standardise_parameters(BT_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.06 | [-0.23, 0.35]
## GroupB_Controls | 0.14 | [-0.20, 0.49]
## GroupC_Intervention | -0.08 | [-0.43, 0.27]
## TimeB_POST_samples | -0.02 | [-0.28, 0.24]
## GroupB_Controls:TimeB_POST_samples | -0.29 | [-0.61, 0.03]
## GroupC_Intervention:TimeB_POST_samples | -0.10 | [-0.42, 0.22]Comparing intervention and psychoed groups
BT_BP_IC1 <- BT_BP %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples") %>%
filter(Group != "A_ECs")
BT_BP_long_IC1 <- BT_BP_IC1 %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_BP_IC1 <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_IC1, REML = TRUE)
summary(BT_MEM_BP_IC1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Group * Time + (1 | ID)
## Data: BT_BP_long_IC1
##
## REML criterion at convergence: 3608.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2671 -0.2557 -0.0891 0.0849 7.5617
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 365.7 19.12
## Residual 321.8 17.94
## Number of obs: 394, groups: ID, 199
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 17.180 2.622 305.755 6.552
## GroupC_Intervention -5.564 3.718 305.755 -1.497
## TimeB_POST_samples -7.796 2.557 195.920 -3.049
## GroupC_Intervention:TimeB_POST_samples 4.806 3.625 195.927 1.326
## Pr(>|t|)
## (Intercept) 2.41e-10 ***
## GroupC_Intervention 0.13552
## TimeB_POST_samples 0.00261 **
## GroupC_Intervention:TimeB_POST_samples 0.18648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpC_I TB_POS
## GrpC_Intrvn -0.705
## TmB_POST_sm -0.480 0.339
## GC_I:TB_POS 0.339 -0.480 -0.705anova (BT_MEM_BP_IC1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 302.30 302.30 1 198.09 0.9393 0.333635
## Time 2848.62 2848.62 1 195.93 8.8514 0.003297 **
## Group:Time 565.61 565.61 1 195.93 1.7575 0.186481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_BP_IC1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.21 | [ 0.01, 0.40]
## GroupC_Intervention | -0.21 | [-0.49, 0.07]
## TimeB_POST_samples | -0.30 | [-0.49, -0.11]
## GroupC_Intervention:TimeB_POST_samples | 0.18 | [-0.09, 0.45]Intervention group
BT_I_p1 <- BT_BP %>%
filter(Group == "C_Intervention")
BT_I_long_p1 <- BT_I_p1 %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_I_p1 <- lmer(BT_Score ~ Time + (1|ID), data = BT_I_long_p1, REML = TRUE)
summary(BT_MEM_I_p1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_I_long_p1
##
## REML criterion at convergence: 1592.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5157 -0.3236 -0.0810 0.1243 6.7260
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 165.17 12.852
## Residual 97.48 9.873
## Number of obs: 196, groups: ID, 99
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.616 1.629 139.893 7.132 4.87e-11 ***
## TimeB_POST_samples -3.009 1.415 97.039 -2.126 0.036 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.427anova (BT_MEM_I_p1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 440.74 440.74 1 97.039 4.5214 0.03601 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_I_p1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.09 | [-0.11, 0.29]
## TimeB_POST_samples | -0.19 | [-0.36, -0.01]Psychoeducation group
BT_C_p1 <- BT_BP %>%
filter(Group == "B_Controls")
BT_C_long_p1 <- BT_C_p1 %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_C_p1 <- lmer(BT_Score ~ Time + (1|ID), data = BT_C_long_p1, REML = TRUE)
summary(BT_MEM_C_p1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_C_long_p1
##
## REML criterion at convergence: 1910.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2017 -0.3063 -0.0615 0.0217 5.8984
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 564.4 23.76
## Residual 543.8 23.32
## Number of obs: 198, groups: ID, 100
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.180 3.329 156.506 5.161 7.36e-07 ***
## TimeB_POST_samples -7.794 3.323 98.568 -2.346 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.492anova (BT_MEM_C_p1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 2991.6 2991.6 1 98.568 5.5017 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(BT_MEM_C_p1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.11 | [-0.08, 0.31]
## TimeB_POST_samples | -0.23 | [-0.43, -0.04]Non-active control group
BT_EC_p1 <- BT_BP %>%
filter(Group == "A_ECs")
BT_EC_long_p1 <- BT_EC_p1 %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")
BT_MEM_EC_p1 <- lmer(BT_Score ~ Time + (1|ID), data = BT_EC_long_p1, REML = TRUE)
summary(BT_MEM_EC_p1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BT_Score ~ Time + (1 | ID)
## Data: BT_EC_long_p1
##
## REML criterion at convergence: 688.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6821 -0.2646 -0.0177 0.1754 3.4099
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 303.44 17.420
## Residual 14.87 3.856
## Number of obs: 94, groups: ID, 47
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 13.5745 2.6024 48.1986 5.216 3.81e-06 ***
## TimeB_POST_samples -0.5106 0.7954 46.0000 -0.642 0.524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_sm -0.153anova (BT_MEM_EC_p1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 6.1277 6.1277 1 46 0.4122 0.5241parameters::standardise_parameters(BT_MEM_EC_p1)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | 0.01 | [-0.28, 0.31]
## TimeB_POST_samples | -0.03 | [-0.12, 0.06]m.ef_BT_p<-emmeans(BT_MEM_BP, "Time", "Group")
eff_size(m.ef_BT_p, sigma = sigma(BT_MEM_BP), edf = df.residual(BT_MEM_BP))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.0315 0.206 364 -0.3742 0.437
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.4808 0.143 364 0.1988 0.763
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_samples - B_POST_samples 0.1848 0.143 364 -0.0972 0.467
##
## sigma used for effect sizes: 16.22
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95GM_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM")
GM_BP_long <- GM_BP %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_BP <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long, REML = TRUE)
summary(GM_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_BP_long
##
## REML criterion at convergence: 1673.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5473 -0.4356 -0.0320 0.4087 2.9611
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.471 1.2128
## Residual 0.614 0.7836
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2042 341.6189 13.418 < 2e-16
## GroupB_Controls 0.4015 0.2477 341.6189 1.621 0.105989
## GroupC_Intervention 0.1629 0.2489 341.6189 0.655 0.513178
## TimeB_POST_GM 0.0400 0.1567 254.5431 0.255 0.798745
## GroupB_Controls:TimeB_POST_GM -0.5306 0.1901 254.5431 -2.791 0.005657
## GroupC_Intervention:TimeB_POST_GM -0.7237 0.1915 254.9544 -3.779 0.000196
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeB_POST_GM
## GroupB_Controls:TimeB_POST_GM **
## GroupC_Intervention:TimeB_POST_GM ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_GM -0.384 0.316 0.315
## GB_C:TB_POS 0.316 -0.384 -0.260 -0.824
## GC_I:TB_POS 0.314 -0.259 -0.383 -0.818 0.674anova (GM_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.0352 1.0176 2 256.34 1.6574 0.1926771
## Time 16.3667 16.3667 1 254.86 26.6558 4.9e-07 ***
## Group:Time 8.8331 4.4165 2 254.92 7.1931 0.0009142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(GM_MEM_BP)## # R2 for Mixed Models
##
## Conditional R2: 0.718
## Marginal R2: 0.043parameters::standardise_parameters(GM_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 1.11e-03 | [-0.27, 0.27]
## GroupB_Controls | 0.27 | [-0.06, 0.60]
## GroupC_Intervention | 0.11 | [-0.22, 0.44]
## TimeB_POST_GM | 0.03 | [-0.18, 0.24]
## GroupB_Controls:TimeB_POST_GM | -0.36 | [-0.61, -0.11]
## GroupC_Intervention:TimeB_POST_GM | -0.49 | [-0.75, -0.24]Comparing non-active controls and intervention groups
GM_I_p_IE <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group != "B_Controls")
GM_I_long_p_IE <- GM_I_p_IE %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_B1W_IE <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_I_long_p_IE, REML = TRUE)
summary(GM_MEM_B1W_IE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_I_long_p_IE
##
## REML criterion at convergence: 988.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.47516 -0.44344 -0.04883 0.35658 2.91081
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.4062 1.1858
## Residual 0.6422 0.8014
## Number of obs: 304, groups: ID, 153
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2024 205.0049 13.537 < 2e-16
## GroupC_Intervention 0.1629 0.2467 205.0049 0.660 0.509739
## TimeB_POST_GM 0.0400 0.1603 149.5750 0.250 0.803264
## GroupC_Intervention:TimeB_POST_GM -0.7238 0.1959 149.8293 -3.695 0.000308
##
## (Intercept) ***
## GroupC_Intervention
## TimeB_POST_GM
## GroupC_Intervention:TimeB_POST_GM ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpC_I TB_POS
## GrpC_Intrvn -0.820
## TmB_POST_GM -0.396 0.325
## GC_I:TB_POS 0.324 -0.395 -0.818anova (GM_MEM_B1W_IE)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.4950 0.4950 1 151.21 0.7707 0.3813846
## Time 6.9372 6.9372 1 149.83 10.8020 0.0012630 **
## Group:Time 8.7685 8.7685 1 149.83 13.6535 0.0003076 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_B1W_IE)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 0.08 | [-0.20, 0.35]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeB_POST_GM | 0.03 | [-0.19, 0.24]
## GroupC_Intervention:TimeB_POST_GM | -0.50 | [-0.76, -0.23]Comparing non-active controls and psychoeducation groups
GM_I_p_PE <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group != "C_Intervention")
GM_I_long_p_PE <- GM_I_p_PE %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_B1W_PE <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_I_long_p_PE, REML = TRUE)
summary(GM_MEM_B1W_PE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_I_long_p_PE
##
## REML criterion at convergence: 990.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44261 -0.43280 -0.01648 0.39801 2.48209
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.6193 1.2725
## Residual 0.5078 0.7126
## Number of obs: 312, groups: ID, 156
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2063 194.9955 13.284 < 2e-16 ***
## GroupB_Controls 0.4015 0.2502 194.9955 1.605 0.11020
## TimeB_POST_GM 0.0400 0.1425 154.0000 0.281 0.77935
## GroupB_Controls:TimeB_POST_GM -0.5306 0.1729 154.0000 -3.069 0.00254 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C TB_POS
## GrpB_Cntrls -0.824
## TmB_POST_GM -0.345 0.285
## GB_C:TB_POS 0.285 -0.345 -0.824anova (GM_MEM_B1W_PE)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.1709 0.1709 1 154 0.3366 0.562661
## Time 3.4486 3.4486 1 154 6.7908 0.010062 *
## Group:Time 4.7819 4.7819 1 154 9.4164 0.002542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_B1W_PE)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------------------
## (Intercept) | -0.08 | [-0.35, 0.20]
## GroupB_Controls | 0.27 | [-0.06, 0.61]
## TimeB_POST_GM | 0.03 | [-0.16, 0.22]
## GroupB_Controls:TimeB_POST_GM | -0.36 | [-0.59, -0.13]Intervention group
GM_I_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "C_Intervention")
GM_I_long_p <- GM_I_p %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_I_p <- lmer(GM_Score ~ Time + (1|ID), data = GM_I_long_p, REML = TRUE)
summary(GM_MEM_I_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_I_long_p
##
## REML criterion at convergence: 677.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1963 -0.4652 -0.1065 0.4509 2.7023
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.2470 1.1167
## Residual 0.7763 0.8811
## Number of obs: 204, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9029 0.1402 147.4341 20.712 < 2e-16 ***
## TimeB_POST_GM -0.6839 0.1238 101.2984 -5.526 2.55e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_GM -0.435anova (GM_MEM_I_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 23.709 23.709 1 101.3 30.539 2.55e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_I_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------
## (Intercept) | 0.23 | [ 0.04, 0.42]
## TimeB_POST_GM | -0.47 | [-0.63, -0.30]Psychoeducation group
GM_C_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "B_Controls")
GM_C_long_p <- GM_C_p %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_C_p <- lmer(GM_Score ~ Time + (1|ID), data = GM_C_long_p, REML = TRUE)
summary(GM_MEM_C_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_C_long_p
##
## REML criterion at convergence: 684.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29531 -0.50719 -0.06417 0.45981 2.33778
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.5642 1.2507
## Residual 0.5738 0.7575
## Number of obs: 212, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1420 136.7834 22.121 < 2e-16 ***
## TimeB_POST_GM -0.4906 0.1040 105.0000 -4.715 7.47e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_GM -0.366anova (GM_MEM_C_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 12.755 12.755 1 105 22.23 7.468e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_C_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------
## (Intercept) | 0.17 | [-0.02, 0.36]
## TimeB_POST_GM | -0.33 | [-0.47, -0.19]Non-active control group
GM_EC_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "A_ECs")
GM_EC_long_p <- GM_EC_p %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_EC_p <- lmer(GM_Score ~ Time + (1|ID), data = GM_EC_long_p, REML = TRUE)
summary(GM_MEM_EC_p)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_EC_long_p
##
## REML criterion at convergence: 302.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.32797 -0.24433 0.00479 0.22845 2.78488
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.7376 1.3182
## Residual 0.3665 0.6054
## Number of obs: 100, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2051 58.2658 13.36 <2e-16 ***
## TimeB_POST_GM 0.0400 0.1211 49.0000 0.33 0.743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmB_POST_GM -0.295anova (GM_MEM_EC_p)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 0.04 0.04 1 49 0.1091 0.7425parameters::standardise_parameters(GM_MEM_EC_p)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------
## (Intercept) | -0.01 | [-0.30, 0.27]
## TimeB_POST_GM | 0.03 | [-0.14, 0.19]# GM - post
m.ef_GM_p<-emmeans(GM_MEM_BP, "Time", "Group")
eff_size(m.ef_GM_p, sigma = sigma(GM_MEM_BP), edf = df.residual(GM_MEM_BP))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - B_POST_GM -0.051 0.200 341 -0.444 0.342
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - B_POST_GM 0.626 0.139 341 0.353 0.899
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - B_POST_GM 0.873 0.143 341 0.591 1.154
##
## sigma used for effect sizes: 0.7836
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95GM_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM")
GM_B1W_long <- GM_B1W %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_B1W <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1W_long, REML = TRUE)
summary(GM_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1W_long
##
## REML criterion at convergence: 1747.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2664 -0.5109 -0.1518 0.4940 2.7870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.038 1.019
## Residual 1.010 1.005
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2024 403.7332 13.539 <2e-16
## GroupB_Controls 0.4015 0.2455 403.7332 1.635 0.1027
## GroupC_Intervention 0.1629 0.2467 403.7332 0.661 0.5093
## TimeC_W1_GM -0.0784 0.2041 253.9941 -0.384 0.7012
## GroupB_Controls:TimeC_W1_GM -0.2376 0.2470 253.3522 -0.962 0.3370
## GroupC_Intervention:TimeC_W1_GM -0.5133 0.2484 253.6544 -2.066 0.0398
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeC_W1_GM
## GroupB_Controls:TimeC_W1_GM
## GroupC_Intervention:TimeC_W1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeC_W1_GM -0.489 0.403 0.401
## GB_C:TC_W1_ 0.404 -0.490 -0.332 -0.826
## GC_I:TC_W1_ 0.402 -0.331 -0.490 -0.822 0.679anova (GM_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.0659 2.5329 2 256.72 2.5080 0.0834210 .
## Time 12.1159 12.1159 1 253.25 11.9969 0.0006256 ***
## Group:Time 4.6763 2.3381 2 253.08 2.3152 0.1008383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(GM_MEM_B1W)## # R2 for Mixed Models
##
## Conditional R2: 0.524
## Marginal R2: 0.035parameters::standardise_parameters(GM_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.31, 0.24]
## GroupB_Controls | 0.28 | [-0.06, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeC_W1_GM | -0.05 | [-0.33, 0.22]
## GroupB_Controls:TimeC_W1_GM | -0.16 | [-0.50, 0.17]
## GroupC_Intervention:TimeC_W1_GM | -0.35 | [-0.69, -0.02]Intervention group
GM_I_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "C_Intervention")
GM_I_long_1w <- GM_I_1w %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_I_1w <- lmer(GM_Score ~ Time + (1|ID), data = GM_I_long_1w, REML = TRUE)
summary(GM_MEM_I_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_I_long_1w
##
## REML criterion at convergence: 669.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0272 -0.5347 -0.1665 0.4495 2.6449
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.9190 0.9586
## Residual 0.8902 0.9435
## Number of obs: 203, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9029 0.1325 160.1284 21.903 < 2e-16 ***
## TimeC_W1_GM -0.5918 0.1330 99.8615 -4.451 2.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeC_W1_GM -0.490anova (GM_MEM_I_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 17.635 17.635 1 99.862 19.809 2.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_I_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | 0.21 | [ 0.02, 0.40]
## TimeC_W1_GM | -0.43 | [-0.62, -0.24]Psychoeducation group
GM_C_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "B_Controls")
GM_C_long_1w <- GM_C_1w %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_C_1w <- lmer(GM_Score ~ Time + (1|ID), data = GM_C_long_1w, REML = TRUE)
summary(GM_MEM_C_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_C_long_1w
##
## REML criterion at convergence: 721.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.91277 -0.54284 -0.07926 0.49810 2.19714
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.2837 1.1330
## Residual 0.9229 0.9607
## Number of obs: 210, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1443 156.1787 21.774 <2e-16 ***
## TimeC_W1_GM -0.3162 0.1330 104.0931 -2.378 0.0192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeC_W1_GM -0.454anova (GM_MEM_C_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 5.2187 5.2187 1 104.09 5.6548 0.01923 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_C_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | 0.10 | [-0.09, 0.30]
## TimeC_W1_GM | -0.21 | [-0.39, -0.04]Non-active control group
GM_EC_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "A_ECs")
GM_EC_long_1w <- GM_EC_1w %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_EC_1w <- lmer(GM_Score ~ Time + (1|ID), data = GM_EC_long_1w, REML = TRUE)
summary(GM_MEM_EC_1w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_EC_long_1w
##
## REML criterion at convergence: 350.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7863 -0.6582 -0.1036 0.4625 2.4339
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.7595 0.8715
## Residual 1.4472 1.2030
## Number of obs: 98, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.21008 86.32366 13.043 <2e-16 ***
## TimeC_W1_GM -0.07678 0.24394 48.82968 -0.315 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeC_W1_GM -0.565anova (GM_MEM_EC_1w)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 0.14335 0.14335 1 48.83 0.0991 0.7543parameters::standardise_parameters(GM_MEM_EC_1w)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.02 | [-0.26, 0.31]
## TimeC_W1_GM | -0.05 | [-0.38, 0.28]# GM - 1w
m.ef_GM_1w<-emmeans(GM_MEM_B1W, "Time", "Group")
eff_size(m.ef_GM_1w, sigma = sigma(GM_MEM_B1W), edf = df.residual(GM_MEM_B1W))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - C_W1_GM 0.078 0.203 403 -0.3213 0.477
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - C_W1_GM 0.314 0.139 403 0.0417 0.587
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - C_W1_GM 0.589 0.142 403 0.3094 0.868
##
## sigma used for effect sizes: 1.005
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95GM_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM")
GM_B1M_long <- GM_B1M %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_B1M <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1M_long, REML = TRUE)
summary(GM_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1M_long
##
## REML criterion at convergence: 1639.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3875 -0.5367 -0.1142 0.4999 2.8661
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.0920 1.0450
## Residual 0.8918 0.9444
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.19919 375.54541 13.756 <2e-16
## GroupB_Controls 0.40151 0.24164 375.54541 1.662 0.0974
## GroupC_Intervention 0.16291 0.24277 375.54541 0.671 0.5026
## TimeD_M1_GM -0.03867 0.19861 235.43449 -0.195 0.8458
## GroupB_Controls:TimeD_M1_GM -0.30728 0.24103 235.51016 -1.275 0.2036
## GroupC_Intervention:TimeD_M1_GM -0.59125 0.24193 235.33298 -2.444 0.0153
##
## (Intercept) ***
## GroupB_Controls .
## GroupC_Intervention
## TimeD_M1_GM
## GroupB_Controls:TimeD_M1_GM
## GroupC_Intervention:TimeD_M1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeD_M1_GM -0.451 0.372 0.370
## GB_C:TD_M1_ 0.372 -0.451 -0.305 -0.824
## GC_I:TD_M1_ 0.370 -0.305 -0.451 -0.821 0.676anova (GM_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3400 2.1700 2 254.05 2.4332 0.089803 .
## Time 11.8930 11.8930 1 235.41 13.3353 0.000321 ***
## Group:Time 5.5615 2.7807 2 235.41 3.1180 0.046079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(GM_MEM_B1M)## # R2 for Mixed Models
##
## Conditional R2: 0.568
## Marginal R2: 0.039parameters::standardise_parameters(GM_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.28 | [-0.05, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeD_M1_GM | -0.03 | [-0.30, 0.25]
## GroupB_Controls:TimeD_M1_GM | -0.21 | [-0.55, 0.12]
## GroupC_Intervention:TimeD_M1_GM | -0.41 | [-0.75, -0.08]Intervention group
GM_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "C_Intervention")
GM_I_long_1m <- GM_I_1m %>%
pivot_longer(cols = c("A_PRE_GM", "D_M1_GM"),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_I_1m <- lmer(GM_Score ~ Time + (1|ID), data = GM_I_long_1m, REML = TRUE)
summary(GM_MEM_I_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_I_long_1m
##
## REML criterion at convergence: 648.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9867 -0.5532 -0.1672 0.4949 2.5917
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.9846 0.9923
## Residual 0.9103 0.9541
## Number of obs: 194, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9029 0.1356 153.6770 21.40 < 2e-16 ***
## TimeD_M1_GM -0.6317 0.1395 94.7042 -4.53 1.72e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeD_M1_GM -0.467anova (GM_MEM_I_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 18.677 18.677 1 94.704 20.517 1.722e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_I_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | 0.22 | [ 0.03, 0.41]
## TimeD_M1_GM | -0.45 | [-0.64, -0.25]Psychoeduction group
GM_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "B_Controls")
GM_C_long_1m <- GM_C_1m %>%
pivot_longer(cols = c("A_PRE_GM", "D_M1_GM"),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_C_1m <- lmer(GM_Score ~ Time + (1|ID), data = GM_C_long_1m, REML = TRUE)
summary(GM_MEM_C_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_C_long_1m
##
## REML criterion at convergence: 675.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.95946 -0.57782 -0.05595 0.48663 1.97716
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.2394 1.113
## Residual 0.8612 0.928
## Number of obs: 199, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1415 0.1408 148.6405 22.32 <2e-16 ***
## TimeD_M1_GM -0.3467 0.1344 95.3134 -2.58 0.0114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeD_M1_GM -0.430anova (GM_MEM_C_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 5.7344 5.7344 1 95.313 6.6585 0.01139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GM_MEM_C_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | 0.11 | [-0.08, 0.30]
## TimeD_M1_GM | -0.24 | [-0.42, -0.06]Non-active control group
GM_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "A_ECs")
GM_EC_long_1m <- GM_EC_1m %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_MEM_EC_1m <- lmer(GM_Score ~ Time + (1|ID), data = GM_EC_long_1m, REML = TRUE)
summary(GM_MEM_EC_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Time + (1 | ID)
## Data: GM_EC_long_1m
##
## REML criterion at convergence: 315.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.33742 -0.58035 0.03805 0.43870 2.84681
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.0070 1.0035
## Residual 0.9155 0.9568
## Number of obs: 94, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.19609 73.62077 13.973 <2e-16 ***
## TimeD_M1_GM -0.03961 0.20106 45.56175 -0.197 0.845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TimeD_M1_GM -0.464anova (GM_MEM_EC_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 0.035534 0.035534 1 45.562 0.0388 0.8447parameters::standardise_parameters(GM_MEM_EC_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.02 | [-0.26, 0.30]
## TimeD_M1_GM | -0.03 | [-0.32, 0.26]# GM - 1m
m.ef_GM_1m <- emmeans(GM_MEM_B1M, "Time", "Group")
eff_size(m.ef_GM_1m, sigma = sigma(GM_MEM_B1M), edf = df.residual(GM_MEM_B1M))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - D_M1_GM 0.0409 0.210 376 -0.3727 0.455
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - D_M1_GM 0.3663 0.145 376 0.0809 0.652
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GM - D_M1_GM 0.6670 0.148 376 0.3762 0.958
##
## sigma used for effect sizes: 0.9444
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95PHQ_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "C_W1_PHQ_total")
PHQ_B1W_long <- PHQ_B1W %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_MEM_B1W <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1W_long, REML = TRUE)
summary(PHQ_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1W_long
##
## REML criterion at convergence: 3069.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.70427 -0.49822 -0.04073 0.44204 3.13898
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 24.53 4.953
## Residual 10.07 3.173
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8318 339.2793 11.974
## GroupB_Controls -0.5166 1.0091 339.2793 -0.512
## GroupC_Intervention 0.7196 1.0138 339.2793 0.710
## TimeC_W1_PHQ_total -0.1012 0.6458 252.0371 -0.157
## GroupB_Controls:TimeC_W1_PHQ_total -0.7976 0.7821 251.8019 -1.020
## GroupC_Intervention:TimeC_W1_PHQ_total -1.3587 0.7858 251.8141 -1.729
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.609
## GroupC_Intervention 0.478
## TimeC_W1_PHQ_total 0.876
## GroupB_Controls:TimeC_W1_PHQ_total 0.309
## GroupC_Intervention:TimeC_W1_PHQ_total 0.085 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_PHQ_ -0.375 0.309 0.308
## GB_C:TC_W1_ 0.309 -0.375 -0.254 -0.826
## GC_I:TC_W1_ 0.308 -0.254 -0.375 -0.822 0.679anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 18.876 9.438 2 257.38 0.9375 0.39294
## Time 75.005 75.005 1 251.69 7.4503 0.00679 **
## Group:Time 30.533 15.266 2 251.61 1.5164 0.22149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(PHQ_MEM_B1W)## # R2 for Mixed Models
##
## Conditional R2: 0.713
## Marginal R2: 0.015parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.07 | [-0.21, 0.34]
## GroupB_Controls | -0.09 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.46]
## TimeC_W1_PHQ_total | -0.02 | [-0.23, 0.20]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.13 | [-0.39, 0.13]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.23 | [-0.49, 0.03]PHQ_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total")
PHQ_B1M_long <- PHQ_B1M %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_MEM_B1M <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long, REML = TRUE)
summary(PHQ_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1M_long
##
## REML criterion at convergence: 3028.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64448 -0.55044 -0.09744 0.49965 2.79846
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.46 4.633
## Residual 15.15 3.892
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8557 367.8141 11.640
## GroupB_Controls -0.5166 1.0381 367.8141 -0.498
## GroupC_Intervention 0.7196 1.0429 367.8141 0.690
## TimeD_M1_PHQ_total 0.6244 0.8271 240.0648 0.755
## GroupB_Controls:TimeD_M1_PHQ_total -1.8086 0.9995 239.3565 -1.810
## GroupC_Intervention:TimeD_M1_PHQ_total -2.9746 1.0045 239.4295 -2.961
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.61902
## GroupC_Intervention 0.49062
## TimeD_M1_PHQ_total 0.45104
## GroupB_Controls:TimeD_M1_PHQ_total 0.07161 .
## GroupC_Intervention:TimeD_M1_PHQ_total 0.00337 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_PHQ_ -0.428 0.353 0.351
## GB_C:TD_M1_ 0.354 -0.430 -0.291 -0.828
## GC_I:TD_M1_ 0.352 -0.291 -0.430 -0.823 0.681anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 35.957 17.979 2 258.66 1.1869 0.30684
## Time 96.897 96.897 1 239.05 6.3966 0.01208 *
## Group:Time 134.059 67.029 2 238.81 4.4250 0.01297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(PHQ_MEM_B1M)## # R2 for Mixed Models
##
## Conditional R2: 0.597
## Marginal R2: 0.026parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.37]
## GroupB_Controls | -0.08 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.45]
## TimeD_M1_PHQ_total | 0.10 | [-0.16, 0.37]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.30 | [-0.62, 0.03]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.49 | [-0.81, -0.16]Intervention group
PHQ_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "C_Intervention")
PHQ_I_long_1m <- PHQ_I_1m %>%
pivot_longer(cols = c("A_PRE_PHQ_total", "D_M1_PHQ_total"),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_MEM_I_1m <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_I_long_1m, REML = TRUE)
summary(PHQ_MEM_I_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Time + (1 | ID)
## Data: PHQ_I_long_1m
##
## REML criterion at convergence: 1192.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.30041 -0.55128 -0.08312 0.51684 2.64008
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.39 4.516
## Residual 13.80 3.714
## Number of obs: 194, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.6796 0.5761 145.9837 18.54 < 2e-16 ***
## TimeD_M1_PHQ_total -2.3508 0.5441 95.7256 -4.32 3.81e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_PHQ_ -0.427anova (PHQ_MEM_I_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 257.49 257.49 1 95.726 18.663 3.811e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(PHQ_MEM_I_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.18 | [-0.01, 0.37]
## TimeD_M1_PHQ_total | -0.39 | [-0.57, -0.21]Psychoeducation group
PHQ_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "B_Controls")
PHQ_C_long_1m <- PHQ_C_1m %>%
pivot_longer(cols = c("A_PRE_PHQ_total", "D_M1_PHQ_total"),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_MEM_C_1m <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_C_long_1m, REML = TRUE)
summary(PHQ_MEM_C_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Time + (1 | ID)
## Data: PHQ_C_long_1m
##
## REML criterion at convergence: 1265.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3651 -0.6137 -0.1412 0.5123 2.5367
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.77 4.447
## Residual 18.70 4.325
## Number of obs: 200, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.4434 0.6025 159.5745 15.675 <2e-16 ***
## TimeD_M1_PHQ_total -1.1978 0.6221 98.5457 -1.926 0.057 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_PHQ_ -0.471anova (PHQ_MEM_C_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 69.341 69.341 1 98.546 3.7077 0.05704 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(PHQ_MEM_C_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | 0.10 | [-0.09, 0.29]
## TimeD_M1_PHQ_total | -0.19 | [-0.39, 0.00]Non-active control group
PHQ_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "A_ECs")
PHQ_EC_long_1m <- PHQ_EC_1m %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_MEM_EC_1m <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_EC_long_1m, REML = TRUE)
summary(PHQ_MEM_EC_1m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Time + (1 | ID)
## Data: PHQ_EC_long_1m
##
## REML criterion at convergence: 564.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8858 -0.4652 -0.1043 0.5389 1.7733
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 27.42 5.236
## Residual 10.19 3.192
## Number of obs: 93, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.9600 0.8673 62.0927 11.48 <2e-16 ***
## TimeD_M1_PHQ_total 0.6818 0.6819 43.8551 1.00 0.323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_PHQ_ -0.345anova (PHQ_MEM_EC_1m)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 10.188 10.188 1 43.855 0.9999 0.3228parameters::standardise_parameters(PHQ_MEM_EC_1m)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.03 | [-0.31, 0.25]
## TimeD_M1_PHQ_total | 0.11 | [-0.11, 0.33]# phq - 1m
m.ef_phq_1m <- emmeans(PHQ_MEM_B1M, "Time", "Group")
eff_size(m.ef_phq_1m, sigma = sigma(PHQ_MEM_B1M), edf = df.residual(PHQ_MEM_B1M))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_PHQ_total - D_M1_PHQ_total -0.160 0.213 366 -0.5786 0.258
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_PHQ_total - D_M1_PHQ_total 0.304 0.145 366 0.0201 0.588
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_PHQ_total - D_M1_PHQ_total 0.604 0.148 366 0.3132 0.894
##
## sigma used for effect sizes: 3.892
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95GAD_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total")
GAD_B1W_long <- GAD_B1W %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_MEM_B1W <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1W_long, REML = TRUE)
summary(GAD_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1W_long
##
## REML criterion at convergence: 3025.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0453 -0.4584 -0.0843 0.4595 3.1840
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.202 4.712
## Residual 9.297 3.049
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.7937 340.7093 10.104
## GroupB_Controls 0.4706 0.9629 340.7093 0.489
## GroupC_Intervention 1.2616 0.9674 340.7093 1.304
## TimeC_W1_GAD_total 0.3104 0.6206 252.2282 0.500
## GroupB_Controls:TimeC_W1_GAD_total -0.9286 0.7516 251.9901 -1.236
## GroupC_Intervention:TimeC_W1_GAD_total -1.3167 0.7551 252.0024 -1.744
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6254
## GroupC_Intervention 0.1931
## TimeC_W1_GAD_total 0.6174
## GroupB_Controls:TimeC_W1_GAD_total 0.2178
## GroupC_Intervention:TimeC_W1_GAD_total 0.0824 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_GAD_ -0.378 0.311 0.310
## GB_C:TC_W1_ 0.312 -0.378 -0.256 -0.826
## GC_I:TC_W1_ 0.310 -0.256 -0.378 -0.822 0.679anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.671 3.8355 2 257.54 0.4125 0.6624
## Time 21.411 21.4112 1 251.88 2.3030 0.1304
## Group:Time 28.314 14.1572 2 251.80 1.5228 0.2201performance::r2(GAD_MEM_B1W)## # R2 for Mixed Models
##
## Conditional R2: 0.707
## Marginal R2: 0.007parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | -0.07 | [-0.35, 0.20]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.11, 0.56]
## TimeC_W1_GAD_total | 0.06 | [-0.16, 0.27]
## GroupB_Controls:TimeC_W1_GAD_total | -0.17 | [-0.43, 0.10]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.23 | [-0.50, 0.03]GAD_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total")
GAD_B1M_long <- GAD_B1M %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_MEM_B1M <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1M_long, REML = TRUE)
summary(GAD_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1M_long
##
## REML criterion at convergence: 2951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26184 -0.51370 -0.07266 0.47118 2.72886
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.51 4.417
## Residual 12.64 3.555
## Number of obs: 486, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.8018 361.1090 10.002
## GroupB_Controls 0.4706 0.9727 361.1090 0.484
## GroupC_Intervention 1.2616 0.9772 361.1090 1.291
## TimeD_M1_GAD_total 1.2587 0.7560 238.1128 1.665
## GroupB_Controls:TimeD_M1_GAD_total -2.2184 0.9147 237.6716 -2.425
## GroupC_Intervention:TimeD_M1_GAD_total -3.0251 0.9181 237.5072 -3.295
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.62884
## GroupC_Intervention 0.19755
## TimeD_M1_GAD_total 0.09724 .
## GroupB_Controls:TimeD_M1_GAD_total 0.01605 *
## GroupC_Intervention:TimeD_M1_GAD_total 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_GAD_ -0.417 0.344 0.342
## GB_C:TD_M1_ 0.345 -0.418 -0.283 -0.826
## GC_I:TD_M1_ 0.343 -0.283 -0.418 -0.823 0.681anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.574 3.787 2 258.32 0.2997 0.741299
## Time 24.554 24.554 1 237.32 1.9431 0.164633
## Group:Time 137.971 68.986 2 237.14 5.4594 0.004808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1performance::r2(GAD_MEM_B1M)## # R2 for Mixed Models
##
## Conditional R2: 0.613
## Marginal R2: 0.016parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.05 | [-0.32, 0.23]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.12, 0.56]
## TimeD_M1_GAD_total | 0.22 | [-0.04, 0.48]
## GroupB_Controls:TimeD_M1_GAD_total | -0.39 | [-0.70, -0.07]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.53 | [-0.85, -0.21]Intervention group
GAD_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "C_Intervention")
GAD_I_long <- GAD_I %>%
pivot_longer(cols = c("A_PRE_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")
GAD_MEM_I <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_I_long, REML = TRUE)
summary(GAD_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_I_long
##
## REML criterion at convergence: 1163.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.87146 -0.49947 -0.05659 0.46378 2.64376
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.51 4.529
## Residual 10.71 3.272
## Number of obs: 194, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.2816 0.5505 138.8771 16.859 < 2e-16 ***
## TimeD_M1_GAD_total -1.7738 0.4802 94.9178 -3.694 0.000369 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_GAD_ -0.393anova (GAD_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 146.07 146.07 1 94.918 13.644 0.0003692 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GAD_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.14 | [-0.05, 0.33]
## TimeD_M1_GAD_total | -0.31 | [-0.48, -0.15]Psychoeducation group
GAD_C <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "B_Controls")
GAD_C_long <- GAD_C %>%
pivot_longer(cols = c("A_PRE_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")
GAD_MEM_C <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_C_long, REML = TRUE)
summary(GAD_MEM_C)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_C_long
##
## REML criterion at convergence: 1228
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0529 -0.5318 -0.1244 0.5077 2.5684
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.46 4.412
## Residual 14.73 3.838
## Number of obs: 199, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.4906 0.5679 151.5975 14.950 <2e-16 ***
## TimeD_M1_GAD_total -0.9713 0.5553 96.1529 -1.749 0.0835 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_GAD_ -0.441anova (GAD_MEM_C)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 45.057 45.057 1 96.153 3.0594 0.08346 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GAD_MEM_C)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | 0.09 | [-0.10, 0.28]
## TimeD_M1_GAD_total | -0.17 | [-0.35, 0.02]Non-active control group
GAD_EC <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "A_ECs")
GAD_EC_long <- GAD_EC %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_MEM_EC <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_EC_long, REML = TRUE)
summary(GAD_MEM_EC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_EC_long
##
## REML criterion at convergence: 556.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29057 -0.48426 -0.04936 0.43148 2.49256
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 17.32 4.162
## Residual 12.26 3.501
## Number of obs: 93, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.0200 0.7692 70.4944 10.427 6.29e-16 ***
## TimeD_M1_GAD_total 1.2538 0.7440 45.7110 1.685 0.0988 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## TmD_M1_GAD_ -0.428anova (GAD_MEM_EC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 34.809 34.809 1 45.711 2.8398 0.09877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(GAD_MEM_EC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.09 | [-0.37, 0.18]
## TimeD_M1_GAD_total | 0.23 | [-0.04, 0.50]# gad - 1m
m.ef_gad_1m <- emmeans(GAD_MEM_B1M, "Time", "Group")
eff_size(m.ef_gad_1m, sigma = sigma(GAD_MEM_B1M), edf = df.residual(GAD_MEM_B1M))## Group = A_ECs:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GAD_total - D_M1_GAD_total -0.354 0.213 359 -0.7731 0.0649
##
## Group = B_Controls:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GAD_total - D_M1_GAD_total 0.270 0.145 359 -0.0155 0.5555
##
## Group = C_Intervention:
## contrast effect.size SE df lower.CL upper.CL
## A_PRE_GAD_total - D_M1_GAD_total 0.497 0.147 359 0.2069 0.7869
##
## sigma used for effect sizes: 3.555
## Degrees-of-freedom method: inherited from kenward-roger when re-gridding
## Confidence level used: 0.95# 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 + excluding outliers
Intervention_group <- changeinvariables %>%
filter(Group == "C_Intervention") %>%
filter(IUS_B1W_change != "-34", IUS_B1W_change != "24", IUS_B1W_change != "-22", IUS_B1W_change != "-21")
Psychoed_group <- changeinvariables %>%
filter(Group == "B_Controls") %>%
filter(IUS_B1W_change != "18")
ECs_group <- changeinvariables %>%
filter(Group == "A_ECs")%>%
filter(IUS_B1M_change != "-11", IUS_B1M_change != "14")Depression at 1 month
# Only significant for the intervention group
cor.test(Intervention_group$IUS_B1M_change, Intervention_group$PHQ_B1M_change)##
## Pearson's product-moment correlation
##
## data: Intervention_group$IUS_B1M_change and Intervention_group$PHQ_B1M_change
## t = 2.657, df = 82, p-value = 0.009475
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0714621 0.4677073
## sample estimates:
## cor
## 0.2815437cor.test(Psychoed_group$IUS_B1M_change, Psychoed_group$PHQ_B1M_change)##
## Pearson's product-moment correlation
##
## data: Psychoed_group$IUS_B1M_change and Psychoed_group$PHQ_B1M_change
## t = 1.8879, df = 89, p-value = 0.0623
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01012637 0.38655121
## sample estimates:
## cor
## 0.1962277cor.test(ECs_group$IUS_B1M_change, ECs_group$PHQ_B1M_change)##
## Pearson's product-moment correlation
##
## data: ECs_group$IUS_B1M_change and ECs_group$PHQ_B1M_change
## t = 1.861, df = 39, p-value = 0.0703
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02418946 0.54532414
## sample estimates:
## cor
## 0.2855863Anxiety at 1 month
# Significant for the intervention and psychoeducation groups (but strongest for intervention)
cor.test(Intervention_group$IUS_B1M_change, Intervention_group$GAD_B1M_change)##
## Pearson's product-moment correlation
##
## data: Intervention_group$IUS_B1M_change and Intervention_group$GAD_B1M_change
## t = 4.4749, df = 82, p-value = 2.441e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2526257 0.6003940
## sample estimates:
## cor
## 0.4430258cor.test(Psychoed_group$IUS_B1M_change, Psychoed_group$GAD_B1M_change)##
## Pearson's product-moment correlation
##
## data: Psychoed_group$IUS_B1M_change and Psychoed_group$GAD_B1M_change
## t = 3.3444, df = 88, p-value = 0.001213
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1383388 0.5076004
## sample estimates:
## cor
## 0.3358094cor.test(ECs_group$IUS_B1M_change, ECs_group$GAD_B1M_change)##
## Pearson's product-moment correlation
##
## data: ECs_group$IUS_B1M_change and ECs_group$GAD_B1M_change
## t = 1.8079, df = 39, p-value = 0.07834
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03234452 0.53956411
## sample estimates:
## cor
## 0.278073Mediation.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 <- lavaan::sem(Mediation.PHQchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1W_change ~
## Group (c1) -0.096 0.086 -1.108 0.268 -0.096 -0.071
## IUS_B1W_change ~
## Group (a1) -0.374 0.076 -4.913 0.000 -0.374 -0.278
## PHQ_B1W_change ~
## IUS_B1W_c (b1) 0.128 0.076 1.686 0.092 0.128 0.128
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.211 0.190 1.109 0.268 0.211 0.211
## .IUS_B1W_change 0.826 0.161 5.135 0.000 0.826 0.827
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1W_change 0.970 0.124 7.850 0.000 0.970 0.974
## .IUS_B1W_change 0.920 0.116 7.943 0.000 0.920 0.923
##
## R-Square:
## Estimate
## PHQ_B1W_change 0.026
## IUS_B1W_change 0.077
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.048 0.029 -1.665 0.096 -0.048 -0.036
## direct -0.096 0.086 -1.108 0.268 -0.096 -0.071
## total -0.144 0.082 -1.754 0.079 -0.144 -0.107Mediation.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 <- lavaan::sem(Mediation.PHQchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.PHQ.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PHQ_B1M_change ~
## Group (c1) -0.174 0.087 -1.994 0.046 -0.174 -0.129
## IUS_B1M_change ~
## Group (a1) -0.413 0.076 -5.465 0.000 -0.413 -0.307
## PHQ_B1M_change ~
## IUS_B1M_c (b1) 0.237 0.087 2.717 0.007 0.237 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.383 0.196 1.950 0.051 0.383 0.383
## .IUS_B1M_change 0.911 0.163 5.574 0.000 0.911 0.913
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PHQ_B1M_change 0.907 0.104 8.708 0.000 0.907 0.909
## .IUS_B1M_change 0.901 0.101 8.897 0.000 0.901 0.906
##
## R-Square:
## Estimate
## PHQ_B1M_change 0.091
## IUS_B1M_change 0.094
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.098 0.040 -2.426 0.015 -0.098 -0.073
## direct -0.174 0.087 -1.994 0.046 -0.174 -0.129
## total -0.272 0.078 -3.464 0.001 -0.272 -0.202Mediation.GADchange.1W <-
'#regressions
GAD_B1W_change ~ c1 * Group
IUS_B1W_change ~ a1 * Group
GAD_B1W_change ~ b1*IUS_B1W_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1W <- sem(Mediation.GADchange.1W, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1W, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 3
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1W_change ~
## Group (c1) -0.059 0.083 -0.713 0.476 -0.059 -0.044
## IUS_B1W_change ~
## Group (a1) -0.374 0.076 -4.913 0.000 -0.374 -0.278
## GAD_B1W_change ~
## IUS_B1W_c (b1) 0.212 0.082 2.592 0.010 0.212 0.212
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.130 0.178 0.731 0.465 0.130 0.130
## .IUS_B1W_change 0.826 0.161 5.135 0.000 0.826 0.827
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1W_change 0.944 0.138 6.823 0.000 0.944 0.948
## .IUS_B1W_change 0.920 0.116 7.943 0.000 0.920 0.923
##
## R-Square:
## Estimate
## GAD_B1W_change 0.052
## IUS_B1W_change 0.077
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.079 0.036 -2.235 0.025 -0.079 -0.059
## direct -0.059 0.083 -0.713 0.476 -0.059 -0.044
## total -0.139 0.079 -1.759 0.079 -0.139 -0.103Mediation.GADchange.1M <-
'#regressions
GAD_B1M_change ~ c1 * Group
IUS_B1M_change ~ a1 * Group
GAD_B1M_change ~ b1*IUS_B1M_change
indirect1 := a1 * b1
direct := c1
total := c1 + (a1 * b1)
'
group.IUS.GAD.1M <- sem(Mediation.GADchange.1M, data=changeinvariables, std.lv=T, std.ov=T, missing='fiml', se='robust', estimator='mlr', auto.var = T)
summary(group.IUS.GAD.1M, standardized=T, rsquare=T)## lavaan 0.6.15 ended normally after 18 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Number of observations 259
## Number of missing patterns 4
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## GAD_B1M_change ~
## Group (c1) -0.147 0.093 -1.587 0.113 -0.147 -0.109
## IUS_B1M_change ~
## Group (a1) -0.413 0.076 -5.463 0.000 -0.413 -0.307
## GAD_B1M_change ~
## IUS_B1M_c (b1) 0.338 0.091 3.714 0.000 0.338 0.337
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.320 0.217 1.473 0.141 0.320 0.319
## .IUS_B1M_change 0.909 0.163 5.566 0.000 0.909 0.912
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .GAD_B1M_change 0.854 0.104 8.239 0.000 0.854 0.852
## .IUS_B1M_change 0.901 0.101 8.900 0.000 0.901 0.906
##
## R-Square:
## Estimate
## GAD_B1M_change 0.148
## IUS_B1M_change 0.094
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirect1 -0.140 0.046 -3.048 0.002 -0.140 -0.103
## direct -0.147 0.093 -1.587 0.113 -0.147 -0.109
## total -0.287 0.084 -3.405 0.001 -0.287 -0.212# 1 week
moderation_GM_PHQ_1W <- lm(PHQ_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1W)##
## Call:
## lm(formula = PHQ_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1562 -2.2737 0.4147 2.7263 12.4817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.2237 1.4201 -1.566 0.1187
## GroupB_Controls 0.5795 1.7734 0.327 0.7441
## GroupC_Intervention 1.8281 1.7812 1.026 0.3058
## A_PRE_GM 0.7632 0.4597 1.660 0.0982 .
## GroupB_Controls:A_PRE_GM -0.5337 0.5529 -0.965 0.3354
## GroupC_Intervention:A_PRE_GM -1.1252 0.5673 -1.983 0.0485 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.481 on 245 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02936, Adjusted R-squared: 0.009554
## F-statistic: 1.482 on 5 and 245 DF, p-value: 0.1961anova(moderation_GM_PHQ_1W)## Analysis of Variance Table
##
## Response: PHQ_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 58.5 29.234 1.4560 0.2352
## A_PRE_GM 1 6.3 6.310 0.3143 0.5756
## Group:A_PRE_GM 2 84.0 42.016 2.0926 0.1256
## Residuals 245 4919.1 20.078# 1 month
moderation_GM_PHQ_1M <- lm(PHQ_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_PHQ_1M)##
## Call:
## lm(formula = PHQ_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.9147 -2.8178 -0.2693 2.9100 19.5368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3893 1.8426 0.211 0.833
## GroupB_Controls -3.2169 2.3213 -1.386 0.167
## GroupC_Intervention -2.5702 2.2870 -1.124 0.262
## A_PRE_GM 0.1475 0.6019 0.245 0.807
## GroupB_Controls:A_PRE_GM 0.4009 0.7291 0.550 0.583
## GroupC_Intervention:A_PRE_GM -0.2152 0.7397 -0.291 0.771
##
## Residual standard error: 5.538 on 222 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.04929, Adjusted R-squared: 0.02788
## F-statistic: 2.302 on 5 and 222 DF, p-value: 0.04579anova(moderation_GM_PHQ_1M)## Analysis of Variance Table
##
## Response: PHQ_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 295.9 147.948 4.8242 0.008894 **
## A_PRE_GM 1 23.5 23.462 0.7650 0.382702
## Group:A_PRE_GM 2 33.6 16.824 0.5486 0.578536
## Residuals 222 6808.2 30.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1 week
moderation_GM_GAD_1W <- lm(GAD_B1W_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1W)##
## Call:
## lm(formula = GAD_B1W_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.697 -1.974 0.186 2.062 14.731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4596 1.3623 -1.071 0.2850
## GroupB_Controls 2.3146 1.7013 1.360 0.1749
## GroupC_Intervention 1.3013 1.7088 0.762 0.4471
## A_PRE_GM 0.6368 0.4410 1.444 0.1500
## GroupB_Controls:A_PRE_GM -1.1092 0.5304 -2.091 0.0375 *
## GroupC_Intervention:A_PRE_GM -0.9231 0.5443 -1.696 0.0911 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.299 on 245 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.03314, Adjusted R-squared: 0.01341
## F-statistic: 1.68 on 5 and 245 DF, p-value: 0.1401anova(moderation_GM_GAD_1W)## Analysis of Variance Table
##
## Response: GAD_B1W_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 54.3 27.131 1.4683 0.2323
## A_PRE_GM 1 17.3 17.305 0.9366 0.3341
## Group:A_PRE_GM 2 83.6 41.801 2.2623 0.1063
## Residuals 245 4527.0 18.478# 1 month
moderation_GM_GAD_1M <- lm(GAD_B1M_change ~ Group*A_PRE_GM, data = changeinvariables)
summary(moderation_GM_GAD_1M)##
## Call:
## lm(formula = GAD_B1M_change ~ Group * A_PRE_GM, data = changeinvariables)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.3797 -2.6023 0.1837 2.5306 17.2648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3786 1.6842 0.225 0.822
## GroupB_Controls -1.5825 2.1219 -0.746 0.457
## GroupC_Intervention -1.2153 2.0904 -0.581 0.562
## A_PRE_GM 0.3566 0.5502 0.648 0.518
## GroupB_Controls:A_PRE_GM -0.2399 0.6668 -0.360 0.719
## GroupC_Intervention:A_PRE_GM -0.7035 0.6761 -1.041 0.299
##
## Residual standard error: 5.062 on 221 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.05464, Adjusted R-squared: 0.03325
## F-statistic: 2.554 on 5 and 221 DF, p-value: 0.02854anova(moderation_GM_GAD_1M)## Analysis of Variance Table
##
## Response: GAD_B1M_change
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 294.1 147.030 5.7385 0.003718 **
## A_PRE_GM 1 0.1 0.092 0.0036 0.952337
## Group:A_PRE_GM 2 33.1 16.547 0.6458 0.525220
## Residuals 221 5662.3 25.621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PRE_IUS_FI_lm <- lm(A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
summary(PRE_IUS_FI_lm)##
## Call:
## lm(formula = A_PRE_IUS_total ~ A_PRE_FI_total, data = Full_data_all)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.1561 -4.1561 0.3806 5.0026 15.3806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.1074 1.2521 23.25 <2e-16 ***
## A_PRE_FI_total 1.2927 0.1148 11.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.265 on 257 degrees of freedom
## Multiple R-squared: 0.3304, Adjusted R-squared: 0.3278
## F-statistic: 126.8 on 1 and 257 DF, p-value: < 2.2e-16anova(PRE_IUS_FI_lm)## Analysis of Variance Table
##
## Response: A_PRE_IUS_total
## Df Sum Sq Mean Sq F value Pr(>F)
## A_PRE_FI_total 1 6692.6 6692.6 126.8 < 2.2e-16 ***
## Residuals 257 13565.1 52.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1cor.test(Full_data_all$A_PRE_IUS_total, Full_data_all$A_PRE_FI_total, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: Full_data_all$A_PRE_IUS_total and Full_data_all$A_PRE_FI_total
## t = 11.26, df = 257, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4870121 0.6510571
## sample estimates:
## cor
## 0.5747811FI_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total")
FI_B1W_long <- FI_B1W %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_MEM_B1W <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1W_long, REML = TRUE)
summary(FI_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1W_long
##
## REML criterion at convergence: 2740.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86590 -0.50210 -0.00798 0.48671 2.80124
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.039 3.007
## Residual 6.577 2.565
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.86000 0.55886 380.24977 17.643
## GroupB_Controls 0.16830 0.67797 380.24977 0.248
## GroupC_Intervention 0.61573 0.68113 380.24977 0.904
## TimeC_W1_FI_total 0.09842 0.52127 252.70546 0.189
## GroupB_Controls:TimeC_W1_FI_total -0.30400 0.63140 252.38770 -0.481
## GroupC_Intervention:TimeC_W1_FI_total -1.05347 0.63439 252.40409 -1.661
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.804
## GroupC_Intervention 0.367
## TimeC_W1_FI_total 0.850
## GroupB_Controls:TimeC_W1_FI_total 0.631
## GroupC_Intervention:TimeC_W1_FI_total 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_FI_t -0.452 0.372 0.370
## GB_C:TC_W1_ 0.373 -0.452 -0.306 -0.826
## GC_I:TC_W1_ 0.371 -0.306 -0.452 -0.822 0.678anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.2043 0.1022 2 256.87 0.0155 0.9846
## Time 14.0165 14.0165 1 252.24 2.1312 0.1456
## Group:Time 23.2052 11.6026 2 252.13 1.7642 0.1734parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.16 | [-0.18, 0.49]
## TimeC_W1_FI_total | 0.02 | [-0.23, 0.28]
## GroupB_Controls:TimeC_W1_FI_total | -0.08 | [-0.39, 0.24]
## GroupC_Intervention:TimeC_W1_FI_total | -0.27 | [-0.58, 0.05]FI_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "D_M1_FI_total")
FI_B1M_long <- FI_B1M %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_MEM_B1M <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1M_long, REML = TRUE)
summary(FI_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1M_long
##
## REML criterion at convergence: 2627.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39593 -0.49332 -0.00673 0.48809 2.50102
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 10.482 3.238
## Residual 5.891 2.427
## Number of obs: 489, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.8600 0.5722 351.6690 17.230
## GroupB_Controls 0.1683 0.6942 351.6690 0.242
## GroupC_Intervention 0.6157 0.6974 351.6690 0.883
## TimeD_M1_FI_total -0.1205 0.5119 237.2371 -0.235
## GroupB_Controls:TimeD_M1_FI_total -0.5991 0.6203 237.0764 -0.966
## GroupC_Intervention:TimeD_M1_FI_total -0.9938 0.6226 236.9204 -1.596
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.809
## GroupC_Intervention 0.378
## TimeD_M1_FI_total 0.814
## GroupB_Controls:TimeD_M1_FI_total 0.335
## GroupC_Intervention:TimeD_M1_FI_total 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_FI_t -0.402 0.332 0.330
## GB_C:TD_M1_ 0.332 -0.403 -0.272 -0.825
## GC_I:TD_M1_ 0.331 -0.273 -0.403 -0.822 0.678anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.403 0.702 2 256.73 0.1191 0.887779
## Time 44.086 44.086 1 236.88 7.4834 0.006698 **
## Group:Time 15.160 7.580 2 236.79 1.2867 0.278109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1parameters::standardise_parameters(FI_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.01 | [-0.27, 0.29]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.15 | [-0.19, 0.49]
## TimeD_M1_FI_total | -0.03 | [-0.28, 0.22]
## GroupB_Controls:TimeD_M1_FI_total | -0.15 | [-0.45, 0.15]
## GroupC_Intervention:TimeD_M1_FI_total | -0.25 | [-0.55, 0.06]full_lmer_IUSbp <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long, REML = TRUE)
null_lmer_IUSbp <- update(full_lmer_IUSbp, formula = ~ . -Time:Group) # null means no interaction effect
BF_BIC_IUSbp <- exp((BIC(null_lmer_IUSbp) - BIC(full_lmer_IUSbp))/2)
BF_BIC_IUSbp # for the interaction## [1] 2612.411M2_lmer_IUSbp <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_BP_long, REML = TRUE)
null_lmer_IUSbp <- update(M2_lmer_IUSbp, formula = ~ . -Group) # null means no group effect
BF_BIC_IUSbp <- exp((BIC(null_lmer_IUSbp) - BIC(M2_lmer_IUSbp))/2)
BF_BIC_IUSbp # for group## [1] 0.02741644M3_lmer_IUSbp <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_BP_long, REML = TRUE)
null_lmer_IUSbp <- update(M3_lmer_IUSbp, formula = ~ . -Time) # null means no time effect
BF_BIC_IUSbp <- exp((BIC(null_lmer_IUSbp) - BIC(M3_lmer_IUSbp))/2)
BF_BIC_IUSbp # for the time## [1] 2.401982e+14IUS_BP_long_I <- IUS_BP %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_BP_long_C <- IUS_BP %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_BP_long_EC <- IUS_BP %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
full_lmer_IUSbp_I <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_BP_long_I, REML = TRUE)
null_lmer_IUSbp_I <- update(full_lmer_IUSbp_I, formula = ~ . -Time)
BF_BIC_IUSbp_I <- exp((BIC(null_lmer_IUSbp_I) - BIC(full_lmer_IUSbp_I))/2)
BF_BIC_IUSbp_I## [1] 2825592191M2_lmer_IUSbp_C <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_BP_long_C, REML = TRUE)
null_lmer_IUSbp_C <- update(M2_lmer_IUSbp_C, formula = ~ . -Time)
BF_BIC_IUSbp_C <- exp((BIC(null_lmer_IUSbp_C) - BIC(M2_lmer_IUSbp_C))/2)
BF_BIC_IUSbp_C## [1] 3029998M3_lmer_IUSbp_EC <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_BP_long_EC, REML = TRUE)
null_lmer_IUSbp_EC <- update(M3_lmer_IUSbp_EC, formula = ~ . -Time)
BF_BIC_IUSbp_EC <- exp((BIC(null_lmer_IUSbp_EC) - BIC(M3_lmer_IUSbp_EC))/2)
BF_BIC_IUSbp_EC## [1] 0.2040359full_lmer_IUSb1w <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long, REML = TRUE)
null_lmer_IUSb1w <- update(full_lmer_IUSb1w, formula = ~ . -Time:Group)
BF_BIC_IUSb1w <- exp((BIC(null_lmer_IUSb1w) - BIC(full_lmer_IUSb1w))/2)
BF_BIC_IUSb1w## [1] 241.0079M2_lmer_IUSb1w <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_B1W_long, REML = TRUE)
null_lmer_IUSb1w <- update(M2_lmer_IUSb1w, formula = ~ . -Group)
BF_BIC_IUSb1w <- exp((BIC(null_lmer_IUSb1w) - BIC(M2_lmer_IUSb1w))/2)
BF_BIC_IUSb1w## [1] 0.02231583M3_lmer_IUSb1w <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_B1W_long, REML = TRUE)
null_lmer_IUSb1w <- update(M3_lmer_IUSb1w, formula = ~ . -Time)
BF_BIC_IUSb1w <- exp((BIC(null_lmer_IUSb1w) - BIC(M3_lmer_IUSb1w))/2)
BF_BIC_IUSb1w## [1] 19872.83IUS_B1w_long_I <- IUS_B1W %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_B1w_long_C <- IUS_B1W %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_B1w_long_EC <- IUS_B1W %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
full_lmer_IUSb1w_I <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1w_long_I, REML = TRUE)
null_lmer_IUSb1w_I <- update(full_lmer_IUSb1w_I, formula = ~ . -Time)
BF_BIC_IUSb1w_I <- exp((BIC(null_lmer_IUSb1w_I) - BIC(full_lmer_IUSb1w_I))/2)
BF_BIC_IUSb1w_I## [1] 151951.7M2_lmer_IUSb1w_C <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1w_long_C, REML = TRUE)
null_lmer_IUSb1w_C <- update(M2_lmer_IUSb1w_C, formula = ~ . -Time)
BF_BIC_IUSb1w_C <- exp((BIC(null_lmer_IUSb1w_C) - BIC(M2_lmer_IUSb1w_C))/2)
BF_BIC_IUSb1w_C## [1] 4.847665M3_lmer_IUSb1w_EC <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1w_long_EC, REML = TRUE)
null_lmer_IUSb1w_EC <- update(M3_lmer_IUSb1w_EC, formula = ~ . -Time)
BF_BIC_IUSb1w_EC <- exp((BIC(null_lmer_IUSb1w_EC) - BIC(M3_lmer_IUSb1w_EC))/2)
BF_BIC_IUSb1w_EC## [1] 0.395221full_lmer_IUSb1m <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1M_long, REML = TRUE)
null_lmer_IUSb1m <- update(full_lmer_IUSb1m, formula = ~ . -Time:Group)
BF_BIC_IUSb1m <- exp((BIC(null_lmer_IUSb1m) - BIC(full_lmer_IUSb1m))/2)
BF_BIC_IUSb1m## [1] 2671.184M2_lmer_IUSb1m <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_B1M_long, REML = TRUE)
null_lmer_IUSb1m <- update(M2_lmer_IUSb1m, formula = ~ . -Group)
BF_BIC_IUSb1m <- exp((BIC(null_lmer_IUSb1m) - BIC(M2_lmer_IUSb1m))/2)
BF_BIC_IUSb1m## [1] 0.02837185M3_lmer_IUSb1m <- lmer(IUS_Score ~ Time + Group + (1|ID), data = IUS_B1M_long, REML = TRUE)
null_lmer_IUSb1m <- update(M3_lmer_IUSb1m, formula = ~ . -Time)
BF_BIC_IUSb1m <- exp((BIC(null_lmer_IUSb1m) - BIC(M3_lmer_IUSb1m))/2)
BF_BIC_IUSb1m## [1] 197.3966IUS_B1m_long_I <- IUS_B1M %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_B1m_long_C <- IUS_B1M %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
IUS_B1m_long_EC <- IUS_B1M %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
full_lmer_IUSb1m_I <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1m_long_I, REML = TRUE)
null_lmer_IUSb1m_I <- update(full_lmer_IUSb1m_I, formula = ~ . -Time)
BF_BIC_IUSb1m_I <- exp((BIC(null_lmer_IUSb1m_I) - BIC(full_lmer_IUSb1m_I))/2)
BF_BIC_IUSb1m_I## [1] 94753.09M2_lmer_IUSb1m_C <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1m_long_C, REML = TRUE)
null_lmer_IUSb1m_C <- update(M2_lmer_IUSb1m_C, formula = ~ . -Time)
BF_BIC_IUSb1m_C <- exp((BIC(null_lmer_IUSb1m_C) - BIC(M2_lmer_IUSb1m_C))/2)
BF_BIC_IUSb1m_C## [1] 1.316734M3_lmer_IUSb1m_EC <- lmer(IUS_Score ~ Time + (1|ID), data = IUS_B1m_long_EC, REML = TRUE)
null_lmer_IUSb1m_EC <- update(M3_lmer_IUSb1m_EC, formula = ~ . -Time)
BF_BIC_IUSb1m_EC <- exp((BIC(null_lmer_IUSb1m_EC) - BIC(M3_lmer_IUSb1m_EC))/2)
BF_BIC_IUSb1m_EC## [1] 8.081472# Interaction - BP
full_lmer_IUSbp_IC <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long_IC, REML = TRUE)
null_lmer_IUSbp_IC <- update(full_lmer_IUSbp_IC, formula = ~ . -Time:Group)
BF_BIC_IUSbp_IC <- exp((BIC(null_lmer_IUSbp_IC) - BIC(full_lmer_IUSbp_IC))/2)
BF_BIC_IUSbp_IC## [1] 2.279354# Interaction - 1W
IUS_B1w_long_IC <- IUS_B1W %>%
filter(Group != "A_ECs") %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
full_lmer_IUSb1w_IC <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1w_long_IC, REML = TRUE)
null_lmer_IUSb1w_IC <- update(full_lmer_IUSb1w_IC, formula = ~ . -Time:Group)
BF_BIC_IUSb1w_IC <- exp((BIC(null_lmer_IUSb1w_IC) - BIC(full_lmer_IUSb1w_IC))/2)
BF_BIC_IUSb1w_IC## [1] 3.039647# Interaction - 1M
IUS_B1m_long_IC <- IUS_B1M %>%
filter(Group != "A_ECs") %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")
full_lmer_IUSb1m_IC <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1m_long_IC, REML = TRUE)
null_lmer_IUSb1m_IC <- update(full_lmer_IUSb1m_IC, formula = ~ . -Time:Group)
BF_BIC_IUSb1m_IC <- exp((BIC(null_lmer_IUSb1m_IC) - BIC(full_lmer_IUSb1m_IC))/2)
BF_BIC_IUSb1m_IC## [1] 3.135853ns_BP_long <- BT_PRE_POST %>%
filter(A_PRE_samples != "0") %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score")full_lmer_nsbp <- lmer(BT_Score ~ Group * Time + (1|ID), data = ns_BP_long, REML = TRUE)
null_lmer_nsbp <- update(full_lmer_nsbp, formula = ~ . -Time:Group) # null means no interaction effect
BF_BIC_nsbp <- exp((BIC(null_lmer_nsbp) - BIC(full_lmer_nsbp))/2)
BF_BIC_nsbp # for the interaction## [1] 4.999097M2_lmer_nsbp <- lmer(BT_Score ~ Time + Group + (1|ID), data = ns_BP_long, REML = TRUE)
null_lmer_nsbp <- update(M2_lmer_nsbp, formula = ~ . -Group) # null means no group effect
BF_BIC_nsbp <- exp((BIC(null_lmer_nsbp) - BIC(M2_lmer_nsbp))/2)
BF_BIC_nsbp # for group## [1] 0.8390539M3_lmer_nsbp <- lmer(BT_Score ~ Time + Group + (1|ID), data = ns_BP_long, REML = TRUE)
null_lmer_nsbp <- update(M3_lmer_nsbp, formula = ~ . -Time) # null means no time effect
BF_BIC_nsbp <- exp((BIC(null_lmer_nsbp) - BIC(M3_lmer_nsbp))/2)
BF_BIC_nsbp # for the time## [1] 50.11713ns_BP_long_it <- ns_BP_long %>%
filter(Group == "C_Intervention")
ns_BP_long_ct <- ns_BP_long %>%
filter(Group == "B_Controls")
ns_BP_long_ect <- ns_BP_long %>%
filter(Group == "A_ECs")
full_lmer_nsbp_It <- lmer(BT_Score ~ Time + (1|ID), data = ns_BP_long_it, REML = TRUE)
null_lmer_nsbp_It <- update(full_lmer_nsbp_It, formula = ~ . -Time)
BF_BIC_nsbp_It <- exp((BIC(null_lmer_nsbp_It) - BIC(full_lmer_nsbp_It))/2)
BF_BIC_nsbp_It # intervention## [1] 5.710564M2_lmer_nsbp_Ct <- lmer(BT_Score ~ Time + (1|ID), data = ns_BP_long_ct, REML = TRUE)
null_lmer_nsbp_Ct <- update(M2_lmer_nsbp_Ct, formula = ~ . -Time)
BF_BIC_nsbp_Ct <- exp((BIC(null_lmer_nsbp_Ct) - BIC(M2_lmer_nsbp_Ct))/2)
BF_BIC_nsbp_Ct # psychoed## [1] 21.76864M3_lmer_nsbp_ECt <- lmer(BT_Score ~ Time + (1|ID), data = ns_BP_long_ect, REML = TRUE)
null_lmer_nsbp_ECt <- update(M3_lmer_nsbp_ECt, formula = ~ . -Time)
BF_BIC_nsbp_ECt <- exp((BIC(null_lmer_nsbp_ECt) - BIC(M3_lmer_nsbp_ECt))/2)
BF_BIC_nsbp_ECt # ecs## [1] 0.5438938ns_BP_long_IP <- ns_BP_long %>%
filter(Group != "A_ECs")
M3_lmer_nsbp_EC <- lmer(BT_Score ~ Group * Time + (1|ID), data = ns_BP_long_IP, REML = TRUE)
null_lmer_nsbp_EC <- update(M3_lmer_nsbp_EC, formula = ~ . -Time:Group)
BF_BIC_nsbp_EC <- exp((BIC(null_lmer_nsbp_EC) - BIC(M3_lmer_nsbp_EC))/2)
BF_BIC_nsbp_EC # psychoed vs intervention## [1] 2.420322full_lmer_BTbp <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long, REML = TRUE)
null_lmer_BTbp <- update(full_lmer_BTbp, formula = ~ . -Time:Group) # null means no interaction effect
BF_BIC_BTbp <- exp((BIC(null_lmer_BTbp) - BIC(full_lmer_BTbp))/2)
BF_BIC_BTbp # for the interaction## [1] 1.086503M2_lmer_BTbp <- lmer(BT_Score ~ Time + Group + (1|ID), data = BT_BP_long, REML = TRUE)
null_lmer_BTbp <- update(M2_lmer_BTbp, formula = ~ . -Group) # null means no group effect
BF_BIC_BTbp <- exp((BIC(null_lmer_BTbp) - BIC(M2_lmer_BTbp))/2)
BF_BIC_BTbp # for group## [1] 0.2687153M3_lmer_BTbp <- lmer(BT_Score ~ Time + Group + (1|ID), data = BT_BP_long, REML = TRUE)
null_lmer_BTbp <- update(M3_lmer_BTbp, formula = ~ . -Time) # null means no time effect
BF_BIC_BTbp <- exp((BIC(null_lmer_BTbp) - BIC(M3_lmer_BTbp))/2)
BF_BIC_BTbp # for the time## [1] 14.95944BT_BP_long_it <- BT_BP_long %>%
filter(Group == "C_Intervention")
BT_BP_long_ct <- BT_BP_long %>%
filter(Group == "B_Controls")
BT_BP_long_ect <- BT_BP_long %>%
filter(Group == "A_ECs")
full_lmer_BTbp_It <- lmer(BT_Score ~ Time + (1|ID), data = BT_BP_long_it, REML = TRUE)
null_lmer_BTbp_It <- update(full_lmer_BTbp_It, formula = ~ . -Time)
BF_BIC_BTbp_It <- exp((BIC(null_lmer_BTbp_It) - BIC(full_lmer_BTbp_It))/2)
BF_BIC_BTbp_It # intervention## [1] 2.348086M2_lmer_BTbp_Ct <- lmer(BT_Score ~ Time + (1|ID), data = BT_BP_long_ct, REML = TRUE)
null_lmer_BTbp_Ct <- update(M2_lmer_BTbp_Ct, formula = ~ . -Time)
BF_BIC_BTbp_Ct <- exp((BIC(null_lmer_BTbp_Ct) - BIC(M2_lmer_BTbp_Ct))/2)
BF_BIC_BTbp_Ct # psychoed## [1] 8.816568M3_lmer_BTbp_ECt <- lmer(BT_Score ~ Time + (1|ID), data = BT_BP_long_ect, REML = TRUE)
null_lmer_BTbp_ECt <- update(M3_lmer_BTbp_ECt, formula = ~ . -Time)
BF_BIC_BTbp_ECt <- exp((BIC(null_lmer_BTbp_ECt) - BIC(M3_lmer_BTbp_ECt))/2)
BF_BIC_BTbp_ECt # ecs## [1] 0.2522305BT_BP_long_IP <- BT_BP_long %>%
filter(Group != "A_ECs")
M3_lmer_BTbp_EC <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_IP, REML = TRUE)
null_lmer_BTbp_EC <- update(M3_lmer_BTbp_EC, formula = ~ . -Time:Group)
BF_BIC_BTbp_EC <- exp((BIC(null_lmer_BTbp_EC) - BIC(M3_lmer_BTbp_EC))/2)
BF_BIC_BTbp_EC # psychoed vs intervention## [1] 1.101577full_lmer_GMbp <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long, REML = TRUE)
null_lmer_GMbp <- update(full_lmer_GMbp, formula = ~ . -Time:Group) # null means no interaction effect
BF_BIC_GMbp <- exp((BIC(null_lmer_GMbp) - BIC(full_lmer_GMbp))/2)
BF_BIC_GMbp # for the interaction## [1] 0.3769363M2_lmer_GMbp <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_BP_long, REML = TRUE)
null_lmer_GMbp <- update(M2_lmer_GMbp, formula = ~ . -Group) # null means no group effect
BF_BIC_GMbp <- exp((BIC(null_lmer_GMbp) - BIC(M2_lmer_GMbp))/2)
BF_BIC_GMbp # for group## [1] 0.002426745M3_lmer_GMbp <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_BP_long, REML = TRUE)
null_lmer_GMbp <- update(M3_lmer_GMbp, formula = ~ . -Time) # null means no time effect
BF_BIC_GMbp <- exp((BIC(null_lmer_GMbp) - BIC(M3_lmer_GMbp))/2)
BF_BIC_GMbp # for the time## [1] 3566215GM_BP_long_I <- GM_BP %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_BP_long_C <- GM_BP %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
GM_BP_long_EC <- GM_BP %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMbp_I <- lmer(GM_Score ~ Time + (1|ID), data = GM_BP_long_I, REML = TRUE)
null_lmer_GMbp_I <- update(full_lmer_GMbp_I, formula = ~ . -Time)
BF_BIC_GMbp_I <- exp((BIC(null_lmer_GMbp_I) - BIC(full_lmer_GMbp_I))/2)
BF_BIC_GMbp_I # intervention## [1] 15556.71M2_lmer_GMbp_C <- lmer(GM_Score ~ Time + (1|ID), data = GM_BP_long_C, REML = TRUE)
null_lmer_GMbp_C <- update(M2_lmer_GMbp_C, formula = ~ . -Time)
BF_BIC_GMbp_C <- exp((BIC(null_lmer_GMbp_C) - BIC(M2_lmer_GMbp_C))/2)
BF_BIC_GMbp_C # psychoed## [1] 470.2298M3_lmer_GMbp_EC <- lmer(GM_Score ~ Time + (1|ID), data = GM_BP_long_EC, REML = TRUE)
null_lmer_GMbp_EC <- update(M3_lmer_GMbp_EC, formula = ~ . -Time)
BF_BIC_GMbp_EC <- exp((BIC(null_lmer_GMbp_EC) - BIC(M3_lmer_GMbp_EC))/2)
BF_BIC_GMbp_EC # ecs## [1] 0.03192482GM_BP_long_IC <- GM_BP %>%
filter(Group != "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMbp_IC <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long_IC, REML = TRUE)
null_lmer_GMbp_IC <- update(full_lmer_GMbp_IC, formula = ~ . -Time:Group)
BF_BIC_GMbp_IC <- exp((BIC(null_lmer_GMbp_IC) - BIC(full_lmer_GMbp_IC))/2)
BF_BIC_GMbp_IC #intervention vs psychoed## [1] 0.04062739GM_BP_long_IE <- GM_BP %>%
filter(Group != "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMbp_IE <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long_IE, REML = TRUE)
null_lmer_GMbp_IE <- update(full_lmer_GMbp_IE, formula = ~ . -Time:Group)
BF_BIC_GMbp_IE <- exp((BIC(null_lmer_GMbp_IE) - BIC(full_lmer_GMbp_IE))/2)
BF_BIC_GMbp_IE #intervention vs ecs## [1] 20.21094GM_BP_long_PE <- GM_BP %>%
filter(Group != "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMbp_PE <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long_PE, REML = TRUE)
null_lmer_GMbp_PE <- update(full_lmer_GMbp_PE, formula = ~ . -Time:Group)
BF_BIC_GMbp_PE <- exp((BIC(null_lmer_GMbp_PE) - BIC(full_lmer_GMbp_PE))/2)
BF_BIC_GMbp_PE #psychoed vs ecs## [1] 2.435914full_lmer_GMb1w <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1W_long, REML = TRUE)
null_lmer_GMb1w <- update(full_lmer_GMb1w, formula = ~ . -Time:Group)
BF_BIC_GMb1w <- exp((BIC(null_lmer_GMb1w) - BIC(full_lmer_GMb1w))/2)
BF_BIC_GMb1w## [1] 0.005563526M2_lmer_GMb1w <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_B1W_long, REML = TRUE)
null_lmer_GMb1w <- update(M2_lmer_GMb1w, formula = ~ . -Group)
BF_BIC_GMb1w <- exp((BIC(null_lmer_GMb1w) - BIC(M2_lmer_GMb1w))/2)
BF_BIC_GMb1w## [1] 0.004970869M3_lmer_GMb1w <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_B1W_long, REML = TRUE)
null_lmer_GMb1w <- update(M3_lmer_GMb1w, formula = ~ . -Time)
BF_BIC_GMb1w <- exp((BIC(null_lmer_GMb1w) - BIC(M3_lmer_GMb1w))/2)
BF_BIC_GMb1w## [1] 58.41477GM_B1w_long_I <- GM_B1W %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_B1w_long_C <- GM_B1W %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_B1w_long_EC <- GM_B1W %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMb1w_I <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1w_long_I, REML = TRUE)
null_lmer_GMb1w_I <- update(full_lmer_GMb1w_I, formula = ~ . -Time)
BF_BIC_GMb1w_I <- exp((BIC(null_lmer_GMb1w_I) - BIC(full_lmer_GMb1w_I))/2)
BF_BIC_GMb1w_I## [1] 199.4642M2_lmer_GMb1w_C <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1w_long_C, REML = TRUE)
null_lmer_GMb1w_C <- update(M2_lmer_GMb1w_C, formula = ~ . -Time)
BF_BIC_GMb1w_C <- exp((BIC(null_lmer_GMb1w_C) - BIC(M2_lmer_GMb1w_C))/2)
BF_BIC_GMb1w_C## [1] 0.3694616M3_lmer_GMb1w_EC <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1w_long_EC, REML = TRUE)
null_lmer_GMb1w_EC <- update(M3_lmer_GMb1w_EC, formula = ~ . -Time)
BF_BIC_GMb1w_EC <- exp((BIC(null_lmer_GMb1w_EC) - BIC(M3_lmer_GMb1w_EC))/2)
BF_BIC_GMb1w_EC## [1] 0.06464413full_lmer_GMb1m <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1M_long, REML = TRUE)
null_lmer_GMb1m <- update(full_lmer_GMb1m, formula = ~ . -Time:Group)
BF_BIC_GMb1m <- exp((BIC(null_lmer_GMb1m) - BIC(full_lmer_GMb1m))/2)
BF_BIC_GMb1m## [1] 0.01232158M2_lmer_GMb1m <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_B1M_long, REML = TRUE)
null_lmer_GMb1m <- update(M2_lmer_GMb1m, formula = ~ . -Group)
BF_BIC_GMb1m <- exp((BIC(null_lmer_GMb1m) - BIC(M2_lmer_GMb1m))/2)
BF_BIC_GMb1m## [1] 0.004456573M3_lmer_GMb1m <- lmer(GM_Score ~ Time + Group + (1|ID), data = GM_B1M_long, REML = TRUE)
null_lmer_GMb1m <- update(M3_lmer_GMb1m, formula = ~ . -Time)
BF_BIC_GMb1m <- exp((BIC(null_lmer_GMb1m) - BIC(M3_lmer_GMb1m))/2)
BF_BIC_GMb1m## [1] 216.1675GM_B1m_long_I <- GM_B1M %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_B1m_long_C <- GM_B1M %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")
GM_B1m_long_EC <- GM_B1M %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")
full_lmer_GMb1m_I <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1m_long_I, REML = TRUE)
null_lmer_GMb1m_I <- update(full_lmer_GMb1m_I, formula = ~ . -Time)
BF_BIC_GMb1m_I <- exp((BIC(null_lmer_GMb1m_I) - BIC(full_lmer_GMb1m_I))/2)
BF_BIC_GMb1m_I## [1] 331.0491M2_lmer_GMb1m_C <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1m_long_C, REML = TRUE)
null_lmer_GMb1m_C <- update(M2_lmer_GMb1m_C, formula = ~ . -Time)
BF_BIC_GMb1m_C <- exp((BIC(null_lmer_GMb1m_C) - BIC(M2_lmer_GMb1m_C))/2)
BF_BIC_GMb1m_C## [1] 0.6108577M3_lmer_GMb1m_EC <- lmer(GM_Score ~ Time + (1|ID), data = GM_B1m_long_EC, REML = TRUE)
null_lmer_GMb1m_EC <- update(M3_lmer_GMb1m_EC, formula = ~ . -Time)
BF_BIC_GMb1m_EC <- exp((BIC(null_lmer_GMb1m_EC) - BIC(M3_lmer_GMb1m_EC))/2)
BF_BIC_GMb1m_EC## [1] 0.05271971full_lmer_PHQb1w <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1W_long, REML = TRUE)
null_lmer_PHQb1w <- update(full_lmer_PHQb1w, formula = ~ . -Time:Group)
BF_BIC_PHQb1w <- exp((BIC(null_lmer_PHQb1w) - BIC(full_lmer_PHQb1w))/2)
BF_BIC_PHQb1w## [1] 0.02531879M2_lmer_PHQb1w <- lmer(PHQ_Score ~ Time + Group + (1|ID), data = PHQ_B1W_long, REML = TRUE)
null_lmer_PHQb1w <- update(M2_lmer_PHQb1w, formula = ~ . -Group)
BF_BIC_PHQb1w <- exp((BIC(null_lmer_PHQb1w) - BIC(M2_lmer_PHQb1w))/2)
BF_BIC_PHQb1w## [1] 0.02038741M3_lmer_PHQb1w <- lmer(PHQ_Score ~ Time + Group + (1|ID), data = PHQ_B1W_long, REML = TRUE)
null_lmer_PHQb1w <- update(M3_lmer_PHQb1w, formula = ~ . -Time)
BF_BIC_PHQb1w <- exp((BIC(null_lmer_PHQb1w) - BIC(M3_lmer_PHQb1w))/2)
BF_BIC_PHQb1w## [1] 9.848049PHQ_B1w_long_I <- PHQ_B1W %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_B1w_long_C <- PHQ_B1W %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_B1w_long_EC <- PHQ_B1W %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
full_lmer_PHQb1w_I <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1w_long_I, REML = TRUE)
null_lmer_PHQb1w_I <- update(full_lmer_PHQb1w_I, formula = ~ . -Time)
BF_BIC_PHQb1w_I <- exp((BIC(null_lmer_PHQb1w_I) - BIC(full_lmer_PHQb1w_I))/2)
BF_BIC_PHQb1w_I## [1] 6.213481M2_lmer_PHQb1w_C <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1w_long_C, REML = TRUE)
null_lmer_PHQb1w_C <- update(M2_lmer_PHQb1w_C, formula = ~ . -Time)
BF_BIC_PHQb1w_C <- exp((BIC(null_lmer_PHQb1w_C) - BIC(M2_lmer_PHQb1w_C))/2)
BF_BIC_PHQb1w_C## [1] 0.6586489M3_lmer_PHQb1w_EC <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1w_long_EC, REML = TRUE)
null_lmer_PHQb1w_EC <- update(M3_lmer_PHQb1w_EC, formula = ~ . -Time)
BF_BIC_PHQb1w_EC <- exp((BIC(null_lmer_PHQb1w_EC) - BIC(M3_lmer_PHQb1w_EC))/2)
BF_BIC_PHQb1w_EC## [1] 0.1382616full_lmer_PHQb1m <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long, REML = TRUE)
null_lmer_PHQb1m <- update(full_lmer_PHQb1m, formula = ~ . -Time:Group)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(full_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 0.7440856M2_lmer_PHQb1m <- lmer(PHQ_Score ~ Time + Group + (1|ID), data = PHQ_B1M_long, REML = TRUE)
null_lmer_PHQb1m <- update(M2_lmer_PHQb1m, formula = ~ . -Group)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(M2_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 0.02448161M3_lmer_PHQb1m <- lmer(PHQ_Score ~ Time + Group + (1|ID), data = PHQ_B1M_long, REML = TRUE)
null_lmer_PHQb1m <- update(M3_lmer_PHQb1m, formula = ~ . -Time)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(M3_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 22.08378PHQ_B1m_long_I <- PHQ_B1M %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_B1m_long_C <- PHQ_B1M %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
PHQ_B1m_long_EC <- PHQ_B1M %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")
full_lmer_PHQb1m_I <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1m_long_I, REML = TRUE)
null_lmer_PHQb1m_I <- update(full_lmer_PHQb1m_I, formula = ~ . -Time)
BF_BIC_PHQb1m_I <- exp((BIC(null_lmer_PHQb1m_I) - BIC(full_lmer_PHQb1m_I))/2)
BF_BIC_PHQb1m_I## [1] 527.3174M2_lmer_PHQb1m_C <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1m_long_C, REML = TRUE)
null_lmer_PHQb1m_C <- update(M2_lmer_PHQb1m_C, formula = ~ . -Time)
BF_BIC_PHQb1m_C <- exp((BIC(null_lmer_PHQb1m_C) - BIC(M2_lmer_PHQb1m_C))/2)
BF_BIC_PHQb1m_C## [1] 0.6946523M3_lmer_PHQb1m_EC <- lmer(PHQ_Score ~ Time + (1|ID), data = PHQ_B1m_long_EC, REML = TRUE)
null_lmer_PHQb1m_EC <- update(M3_lmer_PHQb1m_EC, formula = ~ . -Time)
BF_BIC_PHQb1m_EC <- exp((BIC(null_lmer_PHQb1m_EC) - BIC(M3_lmer_PHQb1m_EC))/2)
BF_BIC_PHQb1m_EC## [1] 0.2919433Intervention vs ECs
PHQ_B1M_IE <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group != "B_Controls")
PHQ_B1M_long_IE <- PHQ_B1M_IE %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")full_lmer_PHQb1m <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long_IE, REML = TRUE)
null_lmer_PHQb1m <- update(full_lmer_PHQb1m, formula = ~ . -Time:Group)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(full_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 22.86168Psychoed vs ECs
PHQ_B1M_PE <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group != "C_Intervention")
PHQ_B1M_long_PE <- PHQ_B1M_PE %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")full_lmer_PHQb1m <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long_PE, REML = TRUE)
null_lmer_PHQb1m <- update(full_lmer_PHQb1m, formula = ~ . -Time:Group)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(full_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 0.6985068Intervention vs Psychoed
PHQ_B1M_IP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group != "A_ECs")
PHQ_B1M_long_IP <- PHQ_B1M_IP %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")full_lmer_PHQb1m <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long_IP, REML = TRUE)
null_lmer_PHQb1m <- update(full_lmer_PHQb1m, formula = ~ . -Time:Group)
BF_BIC_PHQb1m <- exp((BIC(null_lmer_PHQb1m) - BIC(full_lmer_PHQb1m))/2)
BF_BIC_PHQb1m## [1] 0.2772541full_lmer_GADb1w <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1W_long, REML = TRUE)
null_lmer_GADb1w <- update(full_lmer_GADb1w, formula = ~ . -Time:Group)
BF_BIC_GADb1w <- exp((BIC(null_lmer_GADb1w) - BIC(full_lmer_GADb1w))/2)
BF_BIC_GADb1w## [1] 0.02352804M2_lmer_GADb1w <- lmer(GAD_Score ~ Time + Group + (1|ID), data = GAD_B1W_long, REML = TRUE)
null_lmer_GADb1w <- update(M2_lmer_GADb1w, formula = ~ . -Group)
BF_BIC_GADb1w <- exp((BIC(null_lmer_GADb1w) - BIC(M2_lmer_GADb1w))/2)
BF_BIC_GADb1w## [1] 0.01108104M3_lmer_GADb1w <- lmer(GAD_Score ~ Time + Group + (1|ID), data = GAD_B1W_long, REML = TRUE)
null_lmer_GADb1w <- update(M3_lmer_GADb1w, formula = ~ . -Time)
BF_BIC_GADb1w <- exp((BIC(null_lmer_GADb1w) - BIC(M3_lmer_GADb1w))/2)
BF_BIC_GADb1w## [1] 0.3252588GAD_B1w_long_I <- GAD_B1W %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_B1w_long_C <- GAD_B1W %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_B1w_long_EC <- GAD_B1W %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
full_lmer_GADb1w_I <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1w_long_I, REML = TRUE)
null_lmer_GADb1w_I <- update(full_lmer_GADb1w_I, formula = ~ . -Time)
BF_BIC_GADb1w_I <- exp((BIC(null_lmer_GADb1w_I) - BIC(full_lmer_GADb1w_I))/2)
BF_BIC_GADb1w_I## [1] 0.76916M2_lmer_GADb1w_C <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1w_long_C, REML = TRUE)
null_lmer_GADb1w_C <- update(M2_lmer_GADb1w_C, formula = ~ . -Time)
BF_BIC_GADb1w_C <- exp((BIC(null_lmer_GADb1w_C) - BIC(M2_lmer_GADb1w_C))/2)
BF_BIC_GADb1w_C## [1] 0.2132653M3_lmer_GADb1w_EC <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1w_long_EC, REML = TRUE)
null_lmer_GADb1w_EC <- update(M3_lmer_GADb1w_EC, formula = ~ . -Time)
BF_BIC_GADb1w_EC <- exp((BIC(null_lmer_GADb1w_EC) - BIC(M3_lmer_GADb1w_EC))/2)
BF_BIC_GADb1w_EC## [1] 0.1446356full_lmer_GADb1m <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1M_long, REML = TRUE)
null_lmer_GADb1m <- update(full_lmer_GADb1m, formula = ~ . -Time:Group)
BF_BIC_GADb1m <- exp((BIC(null_lmer_GADb1m) - BIC(full_lmer_GADb1m))/2)
BF_BIC_GADb1m## [1] 1.697957M2_lmer_GADb1m <- lmer(GAD_Score ~ Time + Group + (1|ID), data = GAD_B1M_long, REML = TRUE)
null_lmer_GADb1m <- update(M2_lmer_GADb1m, formula = ~ . -Group)
BF_BIC_GADb1m <- exp((BIC(null_lmer_GADb1m) - BIC(M2_lmer_GADb1m))/2)
BF_BIC_GADb1m## [1] 0.009551524M3_lmer_GADb1m <- lmer(GAD_Score ~ Time + Group + (1|ID), data = GAD_B1M_long, REML = TRUE)
null_lmer_GADb1m <- update(M3_lmer_GADb1m, formula = ~ . -Time)
BF_BIC_GADb1m <- exp((BIC(null_lmer_GADb1m) - BIC(M3_lmer_GADb1m))/2)
BF_BIC_GADb1m## [1] 0.9999603GAD_B1m_long_I <- GAD_B1M %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_B1m_long_C <- GAD_B1M %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
GAD_B1m_long_EC <- GAD_B1M %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")
full_lmer_GADb1m_I <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1m_long_I, REML = TRUE)
null_lmer_GADb1m_I <- update(full_lmer_GADb1m_I, formula = ~ . -Time)
BF_BIC_GADb1m_I <- exp((BIC(null_lmer_GADb1m_I) - BIC(full_lmer_GADb1m_I))/2)
BF_BIC_GADb1m_I## [1] 52.05475M2_lmer_GADb1m_C <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1m_long_C, REML = TRUE)
null_lmer_GADb1m_C <- update(M2_lmer_GADb1m_C, formula = ~ . -Time)
BF_BIC_GADb1m_C <- exp((BIC(null_lmer_GADb1m_C) - BIC(M2_lmer_GADb1m_C))/2)
BF_BIC_GADb1m_C## [1] 0.4528085M3_lmer_GADb1m_EC <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_B1m_long_EC, REML = TRUE)
null_lmer_GADb1m_EC <- update(M3_lmer_GADb1m_EC, formula = ~ . -Time)
BF_BIC_GADb1m_EC <- exp((BIC(null_lmer_GADb1m_EC) - BIC(M3_lmer_GADb1m_EC))/2)
BF_BIC_GADb1m_EC## [1] 0.7801084full_lmer_FIb1w <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1W_long, REML = TRUE)
null_lmer_FIb1w <- update(full_lmer_FIb1w, formula = ~ . -Time:Group)
BF_BIC_FIb1w <- exp((BIC(null_lmer_FIb1w) - BIC(full_lmer_FIb1w))/2)
BF_BIC_FIb1w## [1] 0.02112748M2_lmer_FIb1w <- lmer(FI_Score ~ Time + Group + (1|ID), data = FI_B1W_long, REML = TRUE)
null_lmer_FIb1w <- update(M2_lmer_FIb1w, formula = ~ . -Group)
BF_BIC_FIb1w <- exp((BIC(null_lmer_FIb1w) - BIC(M2_lmer_FIb1w))/2)
BF_BIC_FIb1w## [1] 0.003390009M3_lmer_FIb1w <- lmer(FI_Score ~ Time + Group + (1|ID), data = FI_B1W_long, REML = TRUE)
null_lmer_FIb1w <- update(M3_lmer_FIb1w, formula = ~ . -Time)
BF_BIC_FIb1w <- exp((BIC(null_lmer_FIb1w) - BIC(M3_lmer_FIb1w))/2)
BF_BIC_FIb1w## [1] 0.1681013FI_B1w_long_I <- FI_B1W %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_B1w_long_C <- FI_B1W %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_B1w_long_EC <- FI_B1W %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")
full_lmer_FIb1w_I <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1w_long_I, REML = TRUE)
null_lmer_FIb1w_I <- update(full_lmer_FIb1w_I, formula = ~ . -Time)
BF_BIC_FIb1w_I <- exp((BIC(null_lmer_FIb1w_I) - BIC(full_lmer_FIb1w_I))/2)
BF_BIC_FIb1w_I## [1] 1.596137M2_lmer_FIb1w_C <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1w_long_C, REML = TRUE)
null_lmer_FIb1w_C <- update(M2_lmer_FIb1w_C, formula = ~ . -Time)
BF_BIC_FIb1w_C <- exp((BIC(null_lmer_FIb1w_C) - BIC(M2_lmer_FIb1w_C))/2)
BF_BIC_FIb1w_C## [1] 0.07294422M3_lmer_FIb1w_EC <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1w_long_EC, REML = TRUE)
null_lmer_FIb1w_EC <- update(M3_lmer_FIb1w_EC, formula = ~ . -Time)
BF_BIC_FIb1w_EC <- exp((BIC(null_lmer_FIb1w_EC) - BIC(M3_lmer_FIb1w_EC))/2)
BF_BIC_FIb1w_EC## [1] 0.1252424full_lmer_FIb1m <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1M_long, REML = TRUE)
null_lmer_FIb1m <- update(full_lmer_FIb1m, formula = ~ . -Time:Group)
BF_BIC_FIb1m <- exp((BIC(null_lmer_FIb1m) - BIC(full_lmer_FIb1m))/2)
BF_BIC_FIb1m## [1] 0.01319748M2_lmer_FIb1m <- lmer(FI_Score ~ Time + Group + (1|ID), data = FI_B1M_long, REML = TRUE)
null_lmer_FIb1m <- update(M2_lmer_FIb1m, formula = ~ . -Group)
BF_BIC_FIb1m <- exp((BIC(null_lmer_FIb1m) - BIC(M2_lmer_FIb1m))/2)
BF_BIC_FIb1m## [1] 0.004394219M3_lmer_FIb1m <- lmer(FI_Score ~ Time + Group + (1|ID), data = FI_B1M_long, REML = TRUE)
null_lmer_FIb1m <- update(M3_lmer_FIb1m, formula = ~ . -Time)
BF_BIC_FIb1m <- exp((BIC(null_lmer_FIb1m) - BIC(M3_lmer_FIb1m))/2)
BF_BIC_FIb1m## [1] 7.371853FI_B1m_long_I <- FI_B1M %>%
filter(Group == "C_Intervention") %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_B1m_long_C <- FI_B1M %>%
filter(Group == "B_Controls") %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")
FI_B1m_long_EC <- FI_B1M %>%
filter(Group == "A_ECs") %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")
full_lmer_FIb1m_I <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1m_long_I, REML = TRUE)
null_lmer_FIb1m_I <- update(full_lmer_FIb1m_I, formula = ~ . -Time)
BF_BIC_FIb1m_I <- exp((BIC(null_lmer_FIb1m_I) - BIC(full_lmer_FIb1m_I))/2)
BF_BIC_FIb1m_I## [1] 2.89944M2_lmer_FIb1m_C <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1m_long_C, REML = TRUE)
null_lmer_FIb1m_C <- update(M2_lmer_FIb1m_C, formula = ~ . -Time)
BF_BIC_FIb1m_C <- exp((BIC(null_lmer_FIb1m_C) - BIC(M2_lmer_FIb1m_C))/2)
BF_BIC_FIb1m_C## [1] 0.8673062M3_lmer_FIb1m_EC <- lmer(FI_Score ~ Time + (1|ID), data = FI_B1m_long_EC, REML = TRUE)
null_lmer_FIb1m_EC <- update(M3_lmer_FIb1m_EC, formula = ~ . -Time)
BF_BIC_FIb1m_EC <- exp((BIC(null_lmer_FIb1m_EC) - BIC(M3_lmer_FIb1m_EC))/2)
BF_BIC_FIb1m_EC## [1] 0.1167154