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## standardizelibrary(emmeans)Full_data_all <- read_csv("Full_data_all.csv")## New names:
## Rows: 259 Columns: 274
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Prolific_ID, Group dbl (272): ...1, X, ...3, ID, B_IUS_1, B_IUS_2,
## B_IUS_3, B_IUS_4, B_IUS_5, B...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
## • `...1` -> `...3`IUS_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total")## Formatting table as needed
IUS_BP_long <- IUS_BP %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_BP <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_BP_long, REML = TRUE)
summary(IUS_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_BP_long
##
## REML criterion at convergence: 3592.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95161 -0.44757 0.00781 0.41532 2.86841
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 71.13 8.434
## Residual 24.28 4.927
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3814 328.7093 29.738
## GroupB_Controls 0.9483 1.6758 328.7093 0.566
## GroupC_Intervention 1.9880 1.6836 328.7093 1.181
## TimeB_POST_IUS_total -0.2800 0.9854 254.2485 -0.284
## GroupB_Controls:TimeB_POST_IUS_total -3.5879 1.1955 254.2485 -3.001
## GroupC_Intervention:TimeB_POST_IUS_total -6.0437 1.2044 254.6121 -5.018
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.57186
## GroupC_Intervention 0.23854
## TimeB_POST_IUS_total 0.77654
## GroupB_Controls:TimeB_POST_IUS_total 0.00296 **
## GroupC_Intervention:TimeB_POST_IUS_total 9.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TB_POST_IUS -0.357 0.294 0.293
## GB_C:TB_POS 0.294 -0.357 -0.241 -0.824
## GC_I:TB_POS 0.292 -0.241 -0.356 -0.818 0.674anova (IUS_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 10.89 5.45 2 256.08 0.2243 0.7992
## Time 1394.73 1394.73 1 254.53 57.4503 6.459e-13 ***
## Group:Time 617.82 308.91 2 254.58 12.7243 5.407e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_BP,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention","Time (Post)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Intolerance of Uncertainty (IUS-12 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Intolerance of Uncertainty (IUS-12 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | 0.09 | -0.18 – 0.36 | <0.001 |
| Psychoeducation Control | 0.09 | -0.23 – 0.42 | 0.572 |
| Mindset Intervention | 0.20 | -0.13 – 0.53 | 0.238 |
| Time (Post) | -0.03 | -0.22 – 0.17 | 0.776 |
| Psychoeducation Control x Time | -0.36 | -0.59 – -0.12 | 0.003 |
| Mindset Intervention x Time | -0.60 | -0.84 – -0.37 | <0.001 |
| Random Effects | |||
| σ2 | 24.28 | ||
| τ00 ID | 71.13 | ||
| ICC | 0.75 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.056 / 0.760 | ||
parameters::standardise_parameters(IUS_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.36]
## GroupB_Controls | 0.09 | [-0.23, 0.42]
## GroupC_Intervention | 0.20 | [-0.13, 0.53]
## TimeB_POST_IUS_total | -0.03 | [-0.22, 0.17]
## GroupB_Controls:TimeB_POST_IUS_total | -0.36 | [-0.59, -0.12]
## GroupC_Intervention:TimeB_POST_IUS_total | -0.60 | [-0.84, -0.37]IUS_I_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_I_p)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 43.07 | 41.09 – 45.04 | <0.001 |
| Time [B_POST_IUS_total] | -6.32 | -7.93 – -4.71 | <0.001 |
| Random Effects | |||
| σ2 | 33.77 | ||
| τ00 ID | 69.41 | ||
| ICC | 0.67 | ||
| N ID | 103 | ||
| Observations | 204 | ||
| Marginal R2 / Conditional R2 | 0.089 / 0.702 | ||
parameters::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]IUS_C_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_C_p)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.09 – 43.96 | <0.001 |
| Time [B_POST_IUS_total] | -3.87 | -5.07 – -2.67 | <0.001 |
| Random Effects | |||
| σ2 | 19.72 | ||
| τ00 ID | 82.41 | ||
| ICC | 0.81 | ||
| N ID | 106 | ||
| Observations | 212 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.814 | ||
parameters::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]IUS_EC_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.7156sjPlot::tab_model(IUS_MEM_EC_p)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.82 – 43.34 | <0.001 |
| Time [B_POST_IUS_total] | -0.28 | -1.80 – 1.24 | 0.715 |
| Random Effects | |||
| σ2 | 14.59 | ||
| τ00 ID | 50.40 | ||
| ICC | 0.78 | ||
| N ID | 50 | ||
| Observations | 100 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.776 | ||
parameters::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")## Formatting table as needed
IUS_B1W_long <- IUS_B1W %>%
pivot_longer(cols = c(A_PRE_IUS_total, C_W1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1W <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1W_long, REML = TRUE)
summary(IUS_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1W_long
##
## REML criterion at convergence: 3541.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.05745 -0.40449 -0.00329 0.45694 2.90920
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 64.04 8.003
## Residual 24.69 4.969
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3321 334.4251 30.837
## GroupB_Controls 0.9483 1.6161 334.4251 0.587
## GroupC_Intervention 1.9880 1.6236 334.4251 1.224
## TimeC_W1_IUS_total 0.8991 1.0114 251.5502 0.889
## GroupB_Controls:TimeC_W1_IUS_total -2.7707 1.2233 251.1429 -2.265
## GroupC_Intervention:TimeC_W1_IUS_total -5.4023 1.2307 251.3348 -4.390
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5577
## GroupC_Intervention 0.2217
## TimeC_W1_IUS_total 0.3749
## GroupB_Controls:TimeC_W1_IUS_total 0.0244 *
## GroupC_Intervention:TimeC_W1_IUS_total 1.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_IUS_ -0.366 0.302 0.301
## GB_C:TC_W1_ 0.303 -0.368 -0.249 -0.827
## GC_I:TC_W1_ 0.301 -0.248 -0.367 -0.822 0.679anova (IUS_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.54 2.77 2 256.11 0.1122 0.8938692
## Time 372.30 372.30 1 251.08 15.0803 0.0001318 ***
## Group:Time 499.29 249.64 2 250.97 10.1120 5.976e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1W,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Week)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Intolerance of Uncertainty (IUS-12 Score"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Intolerance of Uncertainty (IUS-12 Score | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.01 | -0.28 – 0.27 | <0.001 |
| Psychoeducation Control | 0.10 | -0.23 – 0.43 | 0.558 |
| Mindset Intervention | 0.21 | -0.13 – 0.55 | 0.221 |
| Time (1 Week) | 0.09 | -0.11 – 0.30 | 0.374 |
| Psychoeducation Control x Time | -0.29 | -0.55 – -0.04 | 0.024 |
| Mindset Intervention x Time | -0.57 | -0.82 – -0.31 | <0.001 |
| Random Effects | |||
| σ2 | 24.69 | ||
| τ00 ID | 64.04 | ||
| ICC | 0.72 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.027 / 0.729 | ||
parameters::standardise_parameters(IUS_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -7.24e-03 | [-0.28, 0.27]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.55]
## TimeC_W1_IUS_total | 0.09 | [-0.11, 0.30]
## GroupB_Controls:TimeC_W1_IUS_total | -0.29 | [-0.55, -0.04]
## GroupC_Intervention:TimeC_W1_IUS_total | -0.57 | [-0.82, -0.31]IUS_I_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_I_1w)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 43.07 | 41.20 – 44.94 | <0.001 |
| Time [C_W1_IUS_total] | -4.48 | -6.04 – -2.92 | <0.001 |
| Random Effects | |||
| σ2 | 31.45 | ||
| τ00 ID | 61.29 | ||
| ICC | 0.66 | ||
| N ID | 103 | ||
| Observations | 203 | ||
| Marginal R2 / Conditional R2 | 0.052 / 0.678 | ||
parameters::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]IUS_C_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_C_1w)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.12 – 43.93 | <0.001 |
| Time [C_W1_IUS_total] | -1.88 | -3.21 – -0.54 | 0.006 |
| Random Effects | |||
| σ2 | 23.94 | ||
| τ00 ID | 74.91 | ||
| ICC | 0.76 | ||
| N ID | 106 | ||
| Observations | 210 | ||
| Marginal R2 / Conditional R2 | 0.009 / 0.760 | ||
parameters::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]IUS_EC_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "C_W1_IUS_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.2163sjPlot::tab_model(IUS_MEM_EC_1w)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 38.94 – 43.22 | <0.001 |
| Time [C_W1_IUS_total] | 0.89 | -0.52 – 2.31 | 0.213 |
| Random Effects | |||
| σ2 | 12.25 | ||
| τ00 ID | 45.92 | ||
| ICC | 0.79 | ||
| N ID | 50 | ||
| Observations | 98 | ||
| Marginal R2 / Conditional R2 | 0.003 / 0.790 | ||
parameters::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")## Formatting table as needed
IUS_B1M_long <- IUS_B1M %>%
pivot_longer(cols = c(A_PRE_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_MEM_B1M <- lmer(IUS_Score ~ Group * Time + (1|ID), data = IUS_B1M_long, REML = TRUE)
summary(IUS_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: IUS_Score ~ Group * Time + (1 | ID)
## Data: IUS_B1M_long
##
## REML criterion at convergence: 3427.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.95787 -0.46559 0.01777 0.50003 2.25579
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 59.99 7.745
## Residual 29.75 5.454
## Number of obs: 488, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 41.0800 1.3397 343.4368 30.664
## GroupB_Controls 0.9483 1.6252 343.4368 0.583
## GroupC_Intervention 1.9880 1.6328 343.4368 1.218
## TimeD_M1_IUS_total 2.2079 1.1512 235.9671 1.918
## GroupB_Controls:TimeD_M1_IUS_total -3.9626 1.3951 235.8181 -2.840
## GroupC_Intervention:TimeD_M1_IUS_total -6.9119 1.4023 235.8887 -4.929
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.5599
## GroupC_Intervention 0.2242
## TimeD_M1_IUS_total 0.0563 .
## GroupB_Controls:TimeD_M1_IUS_total 0.0049 **
## GroupC_Intervention:TimeD_M1_IUS_total 1.56e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_IUS_ -0.386 0.318 0.317
## GB_C:TD_M1_ 0.318 -0.386 -0.261 -0.825
## GC_I:TD_M1_ 0.317 -0.261 -0.386 -0.821 0.677anova (IUS_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 28.24 14.12 2 257.23 0.4747 0.622614
## Time 207.73 207.73 1 235.80 6.9828 0.008781 **
## Group:Time 736.40 368.20 2 235.76 12.3767 7.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(IUS_MEM_B1M,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Month)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Intolerance of Uncertainty (IUS-12 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Intolerance of Uncertainty (IUS-12 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.02 | -0.30 – 0.25 | <0.001 |
| Psychoeducation Control | 0.10 | -0.23 – 0.43 | 0.560 |
| Mindset Intervention | 0.21 | -0.13 – 0.54 | 0.224 |
| Time (1 Month) | 0.23 | -0.01 – 0.47 | 0.056 |
| Psychoeducation Control x Time | -0.41 | -0.70 – -0.13 | 0.005 |
| Mindset Intervention x Time | -0.72 | -1.01 – -0.43 | <0.001 |
| Random Effects | |||
| σ2 | 29.75 | ||
| τ00 ID | 59.99 | ||
| ICC | 0.67 | ||
| N ID | 259 | ||
| Observations | 488 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.679 | ||
parameters::standardise_parameters(IUS_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.02 | [-0.30, 0.25]
## GroupB_Controls | 0.10 | [-0.23, 0.43]
## GroupC_Intervention | 0.21 | [-0.13, 0.54]
## TimeD_M1_IUS_total | 0.23 | [-0.01, 0.47]
## GroupB_Controls:TimeD_M1_IUS_total | -0.41 | [-0.70, -0.13]
## GroupC_Intervention:TimeD_M1_IUS_total | -0.72 | [-1.01, -0.43]IUS_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_I_1m)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 43.07 | 41.19 – 44.94 | <0.001 |
| Time [D_M1_IUS_total] | -4.70 | -6.38 – -3.02 | <0.001 |
| Random Effects | |||
| σ2 | 33.76 | ||
| τ00 ID | 59.37 | ||
| ICC | 0.64 | ||
| N ID | 103 | ||
| Observations | 194 | ||
| Marginal R2 / Conditional R2 | 0.056 / 0.658 | ||
parameters::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]IUS_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_C_1m)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 42.03 | 40.08 – 43.97 | <0.001 |
| Time [D_M1_IUS_total] | -1.76 | -3.40 – -0.11 | 0.036 |
| Random Effects | |||
| σ2 | 33.21 | ||
| τ00 ID | 69.93 | ||
| ICC | 0.68 | ||
| N ID | 106 | ||
| Observations | 200 | ||
| Marginal R2 / Conditional R2 | 0.007 / 0.680 | ||
parameters::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]IUS_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_IUS_total", "D_M1_IUS_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(IUS_MEM_EC_1m)| IUS Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 41.08 | 39.02 – 43.14 | <0.001 |
| Time [D_M1_IUS_total] | 2.20 | 0.63 – 3.76 | 0.006 |
| Random Effects | |||
| σ2 | 13.84 | ||
| τ00 ID | 40.03 | ||
| ICC | 0.74 | ||
| N ID | 50 | ||
| Observations | 94 | ||
| Marginal R2 / Conditional R2 | 0.022 / 0.749 | ||
parameters::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: 0.95GM_BP <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM")## Formatting table as needed
GM_BP_long <- GM_BP %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_BP <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_BP_long, REML = TRUE)
summary(GM_MEM_BP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_BP_long
##
## REML criterion at convergence: 1673.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5473 -0.4356 -0.0320 0.4087 2.9611
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.471 1.2128
## Residual 0.614 0.7836
## Number of obs: 516, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2042 341.6189 13.418 < 2e-16
## GroupB_Controls 0.4015 0.2477 341.6189 1.621 0.105989
## GroupC_Intervention 0.1629 0.2489 341.6189 0.655 0.513178
## TimeB_POST_GM 0.0400 0.1567 254.5431 0.255 0.798745
## GroupB_Controls:TimeB_POST_GM -0.5306 0.1901 254.5431 -2.791 0.005657
## GroupC_Intervention:TimeB_POST_GM -0.7237 0.1915 254.9544 -3.779 0.000196
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeB_POST_GM
## GroupB_Controls:TimeB_POST_GM **
## GroupC_Intervention:TimeB_POST_GM ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TB_POS GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmB_POST_GM -0.384 0.316 0.315
## GB_C:TB_POS 0.316 -0.384 -0.260 -0.824
## GC_I:TB_POS 0.314 -0.259 -0.383 -0.818 0.674anova (GM_MEM_BP)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 2.0352 1.0176 2 256.34 1.6574 0.1926771
## Time 16.3667 16.3667 1 254.86 26.6558 4.9e-07 ***
## Group:Time 8.8331 4.4165 2 254.92 7.1931 0.0009142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_BP,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (Post)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Growth Mindsets about Uncertainty Tolerance"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Growth Mindsets about Uncertainty Tolerance | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | 0.00 | -0.27 – 0.27 | <0.001 |
| Psychoeducation Control | 0.27 | -0.06 – 0.60 | 0.106 |
| Mindset Intervention | 0.11 | -0.22 – 0.44 | 0.513 |
| Time (Post) | 0.03 | -0.18 – 0.24 | 0.799 |
| Psychoeducation Control x Time | -0.36 | -0.61 – -0.11 | 0.005 |
| Mindset Intervention x Time | -0.49 | -0.75 – -0.24 | <0.001 |
| Random Effects | |||
| σ2 | 0.61 | ||
| τ00 ID | 1.47 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 516 | ||
| Marginal R2 / Conditional R2 | 0.043 / 0.718 | ||
parameters::standardise_parameters(GM_MEM_BP)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------------------------------
## (Intercept) | 1.11e-03 | [-0.27, 0.27]
## GroupB_Controls | 0.27 | [-0.06, 0.60]
## GroupC_Intervention | 0.11 | [-0.22, 0.44]
## TimeB_POST_GM | 0.03 | [-0.18, 0.24]
## GroupB_Controls:TimeB_POST_GM | -0.36 | [-0.61, -0.11]
## GroupC_Intervention:TimeB_POST_GM | -0.49 | [-0.75, -0.24]GM_I_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_I_p)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.90 | 2.63 – 3.18 | <0.001 |
| Time [B_POST_GM] | -0.68 | -0.93 – -0.44 | <0.001 |
| Random Effects | |||
| σ2 | 0.78 | ||
| τ00 ID | 1.25 | ||
| ICC | 0.62 | ||
| N ID | 103 | ||
| Observations | 204 | ||
| Marginal R2 / Conditional R2 | 0.055 / 0.637 | ||
parameters::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]GM_C_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_C_p)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.86 – 3.42 | <0.001 |
| Time [B_POST_GM] | -0.49 | -0.70 – -0.29 | <0.001 |
| Random Effects | |||
| σ2 | 0.57 | ||
| τ00 ID | 1.56 | ||
| ICC | 0.73 | ||
| N ID | 106 | ||
| Observations | 212 | ||
| Marginal R2 / Conditional R2 | 0.027 / 0.739 | ||
parameters::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]GM_EC_p <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "B_POST_GM") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.7425sjPlot::tab_model(GM_MEM_EC_p)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.33 – 3.15 | <0.001 |
| Time [B_POST_GM] | 0.04 | -0.20 – 0.28 | 0.742 |
| Random Effects | |||
| σ2 | 0.37 | ||
| τ00 ID | 1.74 | ||
| ICC | 0.83 | ||
| N ID | 50 | ||
| Observations | 100 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.826 | ||
parameters::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")## Formatting table as needed
GM_B1W_long <- GM_B1W %>%
pivot_longer(cols = c(A_PRE_GM, C_W1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1W <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1W_long, REML = TRUE)
summary(GM_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1W_long
##
## REML criterion at convergence: 1747.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2664 -0.5109 -0.1518 0.4940 2.7870
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.038 1.019
## Residual 1.010 1.005
## Number of obs: 511, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7400 0.2024 403.7332 13.539 <2e-16
## GroupB_Controls 0.4015 0.2455 403.7332 1.635 0.1027
## GroupC_Intervention 0.1629 0.2467 403.7332 0.661 0.5093
## TimeC_W1_GM -0.0784 0.2041 253.9941 -0.384 0.7012
## GroupB_Controls:TimeC_W1_GM -0.2376 0.2470 253.3522 -0.962 0.3370
## GroupC_Intervention:TimeC_W1_GM -0.5133 0.2484 253.6544 -2.066 0.0398
##
## (Intercept) ***
## GroupB_Controls
## GroupC_Intervention
## TimeC_W1_GM
## GroupB_Controls:TimeC_W1_GM
## GroupC_Intervention:TimeC_W1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeC_W1_GM -0.489 0.403 0.401
## GB_C:TC_W1_ 0.404 -0.490 -0.332 -0.826
## GC_I:TC_W1_ 0.402 -0.331 -0.490 -0.822 0.679anova (GM_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 5.0659 2.5329 2 256.72 2.5080 0.0834210 .
## Time 12.1159 12.1159 1 253.25 11.9969 0.0006256 ***
## Group:Time 4.6763 2.3381 2 253.08 2.3152 0.1008383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1W,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Week)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Growth Mindsets about Uncertainty Tolerance"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE,
emph.p = FALSE)| Growth Mindsets about Uncertainty Tolerance | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.03 | -0.31 – 0.24 | <0.001 |
| Psychoeducation Control | 0.28 | -0.06 – 0.61 | 0.103 |
| Mindset Intervention | 0.11 | -0.22 – 0.45 | 0.509 |
| Time (1 Week) | -0.05 | -0.33 – 0.22 | 0.701 |
| Psychoeducation Control x Time | -0.16 | -0.50 – 0.17 | 0.337 |
| Mindset Intervention x Time | -0.35 | -0.69 – -0.02 | 0.039 |
| Random Effects | |||
| σ2 | 1.01 | ||
| τ00 ID | 1.04 | ||
| ICC | 0.51 | ||
| N ID | 259 | ||
| Observations | 511 | ||
| Marginal R2 / Conditional R2 | 0.035 / 0.524 | ||
parameters::standardise_parameters(GM_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.31, 0.24]
## GroupB_Controls | 0.28 | [-0.06, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeC_W1_GM | -0.05 | [-0.33, 0.22]
## GroupB_Controls:TimeC_W1_GM | -0.16 | [-0.50, 0.17]
## GroupC_Intervention:TimeC_W1_GM | -0.35 | [-0.69, -0.02]GM_I_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_I_1w)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.90 | 2.64 – 3.16 | <0.001 |
| Time [C_W1_GM] | -0.59 | -0.85 – -0.33 | <0.001 |
| Random Effects | |||
| σ2 | 0.89 | ||
| τ00 ID | 0.92 | ||
| ICC | 0.51 | ||
| N ID | 103 | ||
| Observations | 203 | ||
| Marginal R2 / Conditional R2 | 0.046 / 0.531 | ||
parameters::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]GM_C_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_C_1w)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.86 – 3.43 | <0.001 |
| Time [C_W1_GM] | -0.32 | -0.58 – -0.05 | 0.018 |
| Random Effects | |||
| σ2 | 0.92 | ||
| τ00 ID | 1.28 | ||
| ICC | 0.58 | ||
| N ID | 106 | ||
| Observations | 210 | ||
| Marginal R2 / Conditional R2 | 0.011 / 0.586 | ||
parameters::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]GM_EC_1w <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "C_W1_GM") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.7543sjPlot::tab_model(GM_MEM_EC_1w)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.32 – 3.16 | <0.001 |
| Time [C_W1_GM] | -0.08 | -0.56 – 0.41 | 0.754 |
| Random Effects | |||
| σ2 | 1.45 | ||
| τ00 ID | 0.76 | ||
| ICC | 0.34 | ||
| N ID | 50 | ||
| Observations | 98 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.345 | ||
parameters::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 - post
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")## Formatting table as needed
GM_B1M_long <- GM_B1M %>%
pivot_longer(cols = c(A_PRE_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_MEM_B1M <- lmer(GM_Score ~ Group * Time + (1|ID), data = GM_B1M_long, REML = TRUE)
summary(GM_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GM_Score ~ Group * Time + (1 | ID)
## Data: GM_B1M_long
##
## REML criterion at convergence: 1639.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3875 -0.5367 -0.1142 0.4999 2.8661
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1.0920 1.0450
## Residual 0.8918 0.9444
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.74000 0.19919 375.54541 13.756 <2e-16
## GroupB_Controls 0.40151 0.24164 375.54541 1.662 0.0974
## GroupC_Intervention 0.16291 0.24277 375.54541 0.671 0.5026
## TimeD_M1_GM -0.03867 0.19861 235.43449 -0.195 0.8458
## GroupB_Controls:TimeD_M1_GM -0.30728 0.24103 235.51016 -1.275 0.2036
## GroupC_Intervention:TimeD_M1_GM -0.59125 0.24193 235.33298 -2.444 0.0153
##
## (Intercept) ***
## GroupB_Controls .
## GroupC_Intervention
## TimeD_M1_GM
## GroupB_Controls:TimeD_M1_GM
## GroupC_Intervention:TimeD_M1_GM *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TimeD_M1_GM -0.451 0.372 0.370
## GB_C:TD_M1_ 0.372 -0.451 -0.305 -0.824
## GC_I:TD_M1_ 0.370 -0.305 -0.451 -0.821 0.676anova (GM_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 4.3400 2.1700 2 254.05 2.4332 0.089803 .
## Time 11.8930 11.8930 1 235.41 13.3353 0.000321 ***
## Group:Time 5.5615 2.7807 2 235.41 3.1180 0.046079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GM_MEM_B1M,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Month)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Growth Mindsets about Uncertainty Tolerance"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Growth Mindsets about Uncertainty Tolerance | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.03 | -0.30 – 0.25 | <0.001 |
| Psychoeducation Control | 0.28 | -0.05 – 0.61 | 0.097 |
| Mindset Intervention | 0.11 | -0.22 – 0.45 | 0.503 |
| Time (1 Month) | -0.03 | -0.30 – 0.25 | 0.846 |
| Psychoeducation Control x Time | -0.21 | -0.55 – 0.12 | 0.203 |
| Mindset Intervention x Time | -0.41 | -0.75 – -0.08 | 0.015 |
| Random Effects | |||
| σ2 | 0.89 | ||
| τ00 ID | 1.09 | ||
| ICC | 0.55 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.039 / 0.568 | ||
parameters::standardise_parameters(GM_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.28 | [-0.05, 0.61]
## GroupC_Intervention | 0.11 | [-0.22, 0.45]
## TimeD_M1_GM | -0.03 | [-0.30, 0.25]
## GroupB_Controls:TimeD_M1_GM | -0.21 | [-0.55, 0.12]
## GroupC_Intervention:TimeD_M1_GM | -0.41 | [-0.75, -0.08]GM_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_I_1m)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.90 | 2.64 – 3.17 | <0.001 |
| Time [D_M1_GM] | -0.63 | -0.91 – -0.36 | <0.001 |
| Random Effects | |||
| σ2 | 0.91 | ||
| τ00 ID | 0.98 | ||
| ICC | 0.52 | ||
| N ID | 103 | ||
| Observations | 194 | ||
| Marginal R2 / Conditional R2 | 0.050 / 0.544 | ||
parameters::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]GM_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(GM_MEM_C_1m)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 2.86 – 3.42 | <0.001 |
| Time [D_M1_GM] | -0.35 | -0.61 – -0.08 | 0.011 |
| Random Effects | |||
| σ2 | 0.86 | ||
| τ00 ID | 1.24 | ||
| ICC | 0.59 | ||
| N ID | 106 | ||
| Observations | 199 | ||
| Marginal R2 / Conditional R2 | 0.014 / 0.596 | ||
parameters::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]GM_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GM", "D_M1_GM") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.8447sjPlot::tab_model(GM_MEM_EC_1m)| GM Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.74 | 2.35 – 3.13 | <0.001 |
| Time [D_M1_GM] | -0.04 | -0.44 – 0.36 | 0.844 |
| Random Effects | |||
| σ2 | 0.92 | ||
| τ00 ID | 1.01 | ||
| ICC | 0.52 | ||
| N ID | 50 | ||
| Observations | 94 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.524 | ||
parameters::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")## Formatting table as needed
PHQ_B1W_long <- PHQ_B1W %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1W <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1W_long, REML = TRUE)
summary(PHQ_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1W_long
##
## REML criterion at convergence: 3069.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.70427 -0.49822 -0.04073 0.44204 3.13898
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 24.53 4.953
## Residual 10.07 3.173
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8318 339.2793 11.974
## GroupB_Controls -0.5166 1.0091 339.2793 -0.512
## GroupC_Intervention 0.7196 1.0138 339.2793 0.710
## TimeC_W1_PHQ_total -0.1012 0.6458 252.0371 -0.157
## GroupB_Controls:TimeC_W1_PHQ_total -0.7976 0.7821 251.8019 -1.020
## GroupC_Intervention:TimeC_W1_PHQ_total -1.3587 0.7858 251.8141 -1.729
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.609
## GroupC_Intervention 0.478
## TimeC_W1_PHQ_total 0.876
## GroupB_Controls:TimeC_W1_PHQ_total 0.309
## GroupC_Intervention:TimeC_W1_PHQ_total 0.085 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_PHQ_ -0.375 0.309 0.308
## GB_C:TC_W1_ 0.309 -0.375 -0.254 -0.826
## GC_I:TC_W1_ 0.308 -0.254 -0.375 -0.822 0.679anova (PHQ_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 18.876 9.438 2 257.38 0.9375 0.39294
## Time 75.005 75.005 1 251.69 7.4503 0.00679 **
## Group:Time 30.533 15.266 2 251.61 1.5164 0.22149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1W,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Week)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Depression (PHQ-8 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Depression (PHQ-8 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | 0.07 | -0.21 – 0.34 | <0.001 |
| Psychoeducation Control | -0.09 | -0.42 – 0.25 | 0.609 |
| Mindset Intervention | 0.12 | -0.22 – 0.46 | 0.478 |
| Time (1 Week) | -0.02 | -0.23 – 0.20 | 0.875 |
| Psychoeducation Control x Time | -0.13 | -0.39 – 0.13 | 0.308 |
| Mindset Intervention x Time | -0.23 | -0.49 – 0.03 | 0.084 |
| Random Effects | |||
| σ2 | 10.07 | ||
| τ00 ID | 24.53 | ||
| ICC | 0.71 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.015 / 0.713 | ||
parameters::standardise_parameters(PHQ_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | 0.07 | [-0.21, 0.34]
## GroupB_Controls | -0.09 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.46]
## TimeC_W1_PHQ_total | -0.02 | [-0.23, 0.20]
## GroupB_Controls:TimeC_W1_PHQ_total | -0.13 | [-0.39, 0.13]
## GroupC_Intervention:TimeC_W1_PHQ_total | -0.23 | [-0.49, 0.03]PHQ_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total")## Formatting table as needed
PHQ_B1M_long <- PHQ_B1M %>%
pivot_longer(cols = c(A_PRE_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_MEM_B1M <- lmer(PHQ_Score ~ Group * Time + (1|ID), data = PHQ_B1M_long, REML = TRUE)
summary(PHQ_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PHQ_Score ~ Group * Time + (1 | ID)
## Data: PHQ_B1M_long
##
## REML criterion at convergence: 3028.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.64448 -0.55044 -0.09744 0.49965 2.79846
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.46 4.633
## Residual 15.15 3.892
## Number of obs: 487, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.9600 0.8557 367.8141 11.640
## GroupB_Controls -0.5166 1.0381 367.8141 -0.498
## GroupC_Intervention 0.7196 1.0429 367.8141 0.690
## TimeD_M1_PHQ_total 0.6244 0.8271 240.0648 0.755
## GroupB_Controls:TimeD_M1_PHQ_total -1.8086 0.9995 239.3565 -1.810
## GroupC_Intervention:TimeD_M1_PHQ_total -2.9746 1.0045 239.4295 -2.961
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.61902
## GroupC_Intervention 0.49062
## TimeD_M1_PHQ_total 0.45104
## GroupB_Controls:TimeD_M1_PHQ_total 0.07161 .
## GroupC_Intervention:TimeD_M1_PHQ_total 0.00337 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_PHQ_ -0.428 0.353 0.351
## GB_C:TD_M1_ 0.354 -0.430 -0.291 -0.828
## GC_I:TD_M1_ 0.352 -0.291 -0.430 -0.823 0.681anova (PHQ_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 35.957 17.979 2 258.66 1.1869 0.30684
## Time 96.897 96.897 1 239.05 6.3966 0.01208 *
## Group:Time 134.059 67.029 2 238.81 4.4250 0.01297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(PHQ_MEM_B1M,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Month)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Depression (PHQ-8 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Depression (PHQ-8 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | 0.09 | -0.18 – 0.37 | <0.001 |
| Psychoeducation Control | -0.08 | -0.42 – 0.25 | 0.619 |
| Mindset Intervention | 0.12 | -0.22 – 0.45 | 0.491 |
| Time (1 Month) | 0.10 | -0.16 – 0.37 | 0.451 |
| Psychoeducation Control x Time | -0.30 | -0.62 – 0.03 | 0.071 |
| Mindset Intervention x Time | -0.49 | -0.81 – -0.16 | 0.003 |
| Random Effects | |||
| σ2 | 15.15 | ||
| τ00 ID | 21.46 | ||
| ICC | 0.59 | ||
| N ID | 259 | ||
| Observations | 487 | ||
| Marginal R2 / Conditional R2 | 0.026 / 0.597 | ||
parameters::standardise_parameters(PHQ_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | 0.09 | [-0.18, 0.37]
## GroupB_Controls | -0.08 | [-0.42, 0.25]
## GroupC_Intervention | 0.12 | [-0.22, 0.45]
## TimeD_M1_PHQ_total | 0.10 | [-0.16, 0.37]
## GroupB_Controls:TimeD_M1_PHQ_total | -0.30 | [-0.62, 0.03]
## GroupC_Intervention:TimeD_M1_PHQ_total | -0.49 | [-0.81, -0.16]PHQ_I_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(PHQ_MEM_I_1m)| PHQ Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 10.68 | 9.54 – 11.82 | <0.001 |
| Time [D_M1_PHQ_total] | -2.35 | -3.42 – -1.28 | <0.001 |
| Random Effects | |||
| σ2 | 13.80 | ||
| τ00 ID | 20.39 | ||
| ICC | 0.60 | ||
| N ID | 103 | ||
| Observations | 194 | ||
| Marginal R2 / Conditional R2 | 0.039 / 0.612 | ||
parameters::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]PHQ_C_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
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 ' ' 1sjPlot::tab_model(PHQ_MEM_C_1m)| PHQ Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.44 | 8.26 – 10.63 | <0.001 |
| Time [D_M1_PHQ_total] | -1.20 | -2.42 – 0.03 | 0.056 |
| Random Effects | |||
| σ2 | 18.70 | ||
| τ00 ID | 19.77 | ||
| ICC | 0.51 | ||
| N ID | 106 | ||
| Observations | 200 | ||
| Marginal R2 / Conditional R2 | 0.009 / 0.518 | ||
parameters::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]PHQ_EC_1m <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_PHQ_total", "D_M1_PHQ_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
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 0.3228sjPlot::tab_model(PHQ_MEM_EC_1m)| PHQ Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.96 | 8.24 – 11.68 | <0.001 |
| Time [D_M1_PHQ_total] | 0.68 | -0.67 – 2.04 | 0.320 |
| Random Effects | |||
| σ2 | 10.19 | ||
| τ00 ID | 27.42 | ||
| ICC | 0.73 | ||
| N ID | 50 | ||
| Observations | 93 | ||
| Marginal R2 / Conditional R2 | 0.003 / 0.730 | ||
parameters::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: 0.95# Merging across timepoints
GAD_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total")## Formatting table as needed
GAD_B1W_long <- GAD_B1W %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1W <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1W_long, REML = TRUE)
summary(GAD_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1W_long
##
## REML criterion at convergence: 3025.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0453 -0.4584 -0.0843 0.4595 3.1840
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.202 4.712
## Residual 9.297 3.049
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.7937 340.7093 10.104
## GroupB_Controls 0.4706 0.9629 340.7093 0.489
## GroupC_Intervention 1.2616 0.9674 340.7093 1.304
## TimeC_W1_GAD_total 0.3104 0.6206 252.2282 0.500
## GroupB_Controls:TimeC_W1_GAD_total -0.9286 0.7516 251.9901 -1.236
## GroupC_Intervention:TimeC_W1_GAD_total -1.3167 0.7551 252.0024 -1.744
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.6254
## GroupC_Intervention 0.1931
## TimeC_W1_GAD_total 0.6174
## GroupB_Controls:TimeC_W1_GAD_total 0.2178
## GroupC_Intervention:TimeC_W1_GAD_total 0.0824 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_GAD_ -0.378 0.311 0.310
## GB_C:TC_W1_ 0.312 -0.378 -0.256 -0.826
## GC_I:TC_W1_ 0.310 -0.256 -0.378 -0.822 0.679anova (GAD_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.671 3.8355 2 257.54 0.4125 0.6624
## Time 21.411 21.4112 1 251.88 2.3030 0.1304
## Group:Time 28.314 14.1572 2 251.80 1.5228 0.2201sjPlot::tab_model(GAD_MEM_B1W,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Week)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Anxiety (GAD-7 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Anxiety (GAD-7 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.07 | -0.35 – 0.20 | <0.001 |
| Psychoeducation Control | 0.08 | -0.25 – 0.42 | 0.625 |
| Mindset Intervention | 0.22 | -0.11 – 0.56 | 0.193 |
| Time (1 Week) | 0.06 | -0.16 – 0.27 | 0.617 |
| Psychoeducation Control x Time | -0.17 | -0.43 – 0.10 | 0.217 |
| Mindset Intervention x Time | -0.23 | -0.50 – 0.03 | 0.082 |
| Random Effects | |||
| σ2 | 9.30 | ||
| τ00 ID | 22.20 | ||
| ICC | 0.70 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.007 / 0.707 | ||
parameters::standardise_parameters(GAD_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -------------------------------------------------------------------
## (Intercept) | -0.07 | [-0.35, 0.20]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.11, 0.56]
## TimeC_W1_GAD_total | 0.06 | [-0.16, 0.27]
## GroupB_Controls:TimeC_W1_GAD_total | -0.17 | [-0.43, 0.10]
## GroupC_Intervention:TimeC_W1_GAD_total | -0.23 | [-0.50, 0.03]# Merging across timepoints
GAD_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "D_M1_GAD_total")## Formatting table as needed
GAD_B1M_long <- GAD_B1M %>%
pivot_longer(cols = c(A_PRE_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_B1M <- lmer(GAD_Score ~ Group * Time + (1|ID), data = GAD_B1M_long, REML = TRUE)
summary(GAD_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Group * Time + (1 | ID)
## Data: GAD_B1M_long
##
## REML criterion at convergence: 2951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26184 -0.51370 -0.07266 0.47118 2.72886
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 19.51 4.417
## Residual 12.64 3.555
## Number of obs: 486, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.0200 0.8018 361.1090 10.002
## GroupB_Controls 0.4706 0.9727 361.1090 0.484
## GroupC_Intervention 1.2616 0.9772 361.1090 1.291
## TimeD_M1_GAD_total 1.2587 0.7560 238.1128 1.665
## GroupB_Controls:TimeD_M1_GAD_total -2.2184 0.9147 237.6716 -2.425
## GroupC_Intervention:TimeD_M1_GAD_total -3.0251 0.9181 237.5072 -3.295
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## GroupB_Controls 0.62884
## GroupC_Intervention 0.19755
## TimeD_M1_GAD_total 0.09724 .
## GroupB_Controls:TimeD_M1_GAD_total 0.01605 *
## GroupC_Intervention:TimeD_M1_GAD_total 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_GAD_ -0.417 0.344 0.342
## GB_C:TD_M1_ 0.345 -0.418 -0.283 -0.826
## GC_I:TD_M1_ 0.343 -0.283 -0.418 -0.823 0.681anova (GAD_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 7.574 3.787 2 258.32 0.2997 0.741299
## Time 24.554 24.554 1 237.32 1.9431 0.164633
## Group:Time 137.971 68.986 2 237.14 5.4594 0.004808 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_B1M,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Month)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Anxiety (GAD-7 Score)"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Anxiety (GAD-7 Score) | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.05 | -0.32 – 0.23 | <0.001 |
| Psychoeducation Control | 0.08 | -0.25 – 0.42 | 0.629 |
| Mindset Intervention | 0.22 | -0.12 – 0.56 | 0.197 |
| Time (1 Month) | 0.22 | -0.04 – 0.48 | 0.097 |
| Psychoeducation Control x Time | -0.39 | -0.70 – -0.07 | 0.016 |
| Mindset Intervention x Time | -0.53 | -0.85 – -0.21 | 0.001 |
| Random Effects | |||
| σ2 | 12.64 | ||
| τ00 ID | 19.51 | ||
| ICC | 0.61 | ||
| N ID | 259 | ||
| Observations | 486 | ||
| Marginal R2 / Conditional R2 | 0.016 / 0.613 | ||
parameters::standardise_parameters(GAD_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## --------------------------------------------------------------------
## (Intercept) | -0.05 | [-0.32, 0.23]
## GroupB_Controls | 0.08 | [-0.25, 0.42]
## GroupC_Intervention | 0.22 | [-0.12, 0.56]
## TimeD_M1_GAD_total | 0.22 | [-0.04, 0.48]
## GroupB_Controls:TimeD_M1_GAD_total | -0.39 | [-0.70, -0.07]
## GroupC_Intervention:TimeD_M1_GAD_total | -0.53 | [-0.85, -0.21]GAD_I <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "C_Intervention")## Formatting table as needed
GAD_I_long <- GAD_I %>%
pivot_longer(cols = c("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_I <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_I_long, REML = TRUE)
summary(GAD_MEM_I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_I_long
##
## REML criterion at convergence: 1726.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8905 -0.4758 -0.0769 0.5231 2.7194
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 21.53 4.640
## Residual 10.86 3.296
## Number of obs: 294, groups: ID, 103
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.2816 0.5608 160.4682 16.552 < 2e-16 ***
## TimeC_W1_GAD_total -1.0187 0.4640 190.5491 -2.195 0.029351 *
## TimeD_M1_GAD_total -1.8024 0.4800 192.1943 -3.755 0.000229 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_GAD_ -0.405
## TmD_M1_GAD_ -0.392 0.472anova (GAD_MEM_I)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 155.64 77.819 2 191.83 7.1649 0.0009978 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(GAD_MEM_I)| GAD Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 9.28 | 8.18 – 10.39 | <0.001 |
| Time [C_W1_GAD_total] | -1.02 | -1.93 – -0.11 | 0.029 |
| Time [D_M1_GAD_total] | -1.80 | -2.75 – -0.86 | <0.001 |
| Random Effects | |||
| σ2 | 10.86 | ||
| τ00 ID | 21.53 | ||
| ICC | 0.66 | ||
| N ID | 103 | ||
| Observations | 294 | ||
| Marginal R2 / Conditional R2 | 0.016 / 0.670 | ||
parameters::standardise_parameters(GAD_MEM_I)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.15 | [-0.04, 0.34]
## TimeC_W1_GAD_total | -0.18 | [-0.34, -0.02]
## TimeD_M1_GAD_total | -0.31 | [-0.48, -0.15]GAD_C <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "B_Controls")## Formatting table as needed
GAD_C_long <- GAD_C %>%
pivot_longer(cols = c("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total"),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_C <- lmer(GAD_Score ~ Time + (1|ID), data = GAD_C_long, REML = TRUE)
summary(GAD_MEM_C)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: GAD_Score ~ Time + (1 | ID)
## Data: GAD_C_long
##
## REML criterion at convergence: 1788.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7651 -0.4670 -0.1016 0.5054 3.2095
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 22.48 4.741
## Residual 11.47 3.387
## Number of obs: 302, groups: ID, 106
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.4906 0.5659 164.7207 15.003 <2e-16 ***
## TimeC_W1_GAD_total -0.6425 0.4703 195.2577 -1.366 0.1734
## TimeD_M1_GAD_total -0.9632 0.4877 197.0825 -1.975 0.0497 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_GAD_ -0.407
## TmD_M1_GAD_ -0.392 0.474anova (GAD_MEM_C)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 47.478 23.739 2 196.44 2.0693 0.129sjPlot::tab_model(GAD_MEM_C)| GAD Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.49 | 7.38 – 9.60 | <0.001 |
| Time [C_W1_GAD_total] | -0.64 | -1.57 – 0.28 | 0.173 |
| Time [D_M1_GAD_total] | -0.96 | -1.92 – -0.00 | 0.049 |
| Random Effects | |||
| σ2 | 11.47 | ||
| τ00 ID | 22.48 | ||
| ICC | 0.66 | ||
| N ID | 106 | ||
| Observations | 302 | ||
| Marginal R2 / Conditional R2 | 0.005 / 0.664 | ||
parameters::standardise_parameters(GAD_MEM_C)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------
## (Intercept) | 0.09 | [-0.10, 0.29]
## TimeC_W1_GAD_total | -0.11 | [-0.27, 0.05]
## TimeD_M1_GAD_total | -0.17 | [-0.33, 0.00]GAD_EC <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total") %>%
filter(Group == "A_ECs")## Formatting table as needed
GAD_EC_long <- GAD_EC %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_MEM_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: 812.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4036 -0.4894 -0.0713 0.4865 3.4462
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 18.47 4.297
## Residual 10.24 3.200
## Number of obs: 141, groups: ID, 50
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.0200 0.7577 79.1032 10.584 <2e-16 ***
## TimeC_W1_GAD_total 0.4472 0.6492 90.0523 0.689 0.493
## TimeD_M1_GAD_total 1.3070 0.6753 91.0193 1.935 0.056 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TC_W1_
## TmC_W1_GAD_ -0.416
## TmD_M1_GAD_ -0.400 0.465anova (GAD_MEM_EC)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Time 38.943 19.472 2 90.832 1.9017 0.1552sjPlot::tab_model(GAD_MEM_EC)| GAD Score | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 8.02 | 6.52 – 9.52 | <0.001 |
| Time [C_W1_GAD_total] | 0.45 | -0.84 – 1.73 | 0.492 |
| Time [D_M1_GAD_total] | 1.31 | -0.03 – 2.64 | 0.055 |
| Random Effects | |||
| σ2 | 10.24 | ||
| τ00 ID | 18.47 | ||
| ICC | 0.64 | ||
| N ID | 50 | ||
| Observations | 141 | ||
| Marginal R2 / Conditional R2 | 0.010 / 0.647 | ||
parameters::standardise_parameters(GAD_MEM_EC)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------------
## (Intercept) | -0.09 | [-0.36, 0.19]
## TimeC_W1_GAD_total | 0.08 | [-0.16, 0.32]
## TimeD_M1_GAD_total | 0.24 | [-0.01, 0.49]# 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# Merging across timepoints
FI_B1W <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "C_W1_FI_total")## Formatting table as needed
FI_B1W_long <- FI_B1W %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1W <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1W_long, REML = TRUE)
summary(FI_MEM_B1W)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1W_long
##
## REML criterion at convergence: 2740.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86590 -0.50210 -0.00798 0.48671 2.80124
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.039 3.007
## Residual 6.577 2.565
## Number of obs: 510, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.86000 0.55886 380.24977 17.643
## GroupB_Controls 0.16830 0.67797 380.24977 0.248
## GroupC_Intervention 0.61573 0.68113 380.24977 0.904
## TimeC_W1_FI_total 0.09842 0.52127 252.70546 0.189
## GroupB_Controls:TimeC_W1_FI_total -0.30400 0.63140 252.38770 -0.481
## GroupC_Intervention:TimeC_W1_FI_total -1.05347 0.63439 252.40409 -1.661
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.804
## GroupC_Intervention 0.367
## TimeC_W1_FI_total 0.850
## GroupB_Controls:TimeC_W1_FI_total 0.631
## GroupC_Intervention:TimeC_W1_FI_total 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TC_W1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmC_W1_FI_t -0.452 0.372 0.370
## GB_C:TC_W1_ 0.373 -0.452 -0.306 -0.826
## GC_I:TC_W1_ 0.371 -0.306 -0.452 -0.822 0.678anova (FI_MEM_B1W)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.2043 0.1022 2 256.87 0.0155 0.9846
## Time 14.0165 14.0165 1 252.24 2.1312 0.1456
## Group:Time 23.2052 11.6026 2 252.13 1.7642 0.1734sjPlot::tab_model(FI_MEM_B1W,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Week)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Functional Impairment"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Functional Impairment | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | -0.03 | -0.30 – 0.25 | <0.001 |
| Psychoeducation Control | 0.04 | -0.29 – 0.38 | 0.804 |
| Mindset Intervention | 0.16 | -0.18 – 0.49 | 0.366 |
| Time (1 Week) | 0.02 | -0.23 – 0.28 | 0.850 |
| Psychoeducation Control x Time | -0.08 | -0.39 – 0.24 | 0.630 |
| Mindset Intervention x Time | -0.27 | -0.58 – 0.05 | 0.097 |
| Random Effects | |||
| σ2 | 6.58 | ||
| τ00 ID | 9.04 | ||
| ICC | 0.58 | ||
| N ID | 259 | ||
| Observations | 510 | ||
| Marginal R2 / Conditional R2 | 0.006 / 0.581 | ||
parameters::standardise_parameters(FI_MEM_B1W)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | -0.03 | [-0.30, 0.25]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.16 | [-0.18, 0.49]
## TimeC_W1_FI_total | 0.02 | [-0.23, 0.28]
## GroupB_Controls:TimeC_W1_FI_total | -0.08 | [-0.39, 0.24]
## GroupC_Intervention:TimeC_W1_FI_total | -0.27 | [-0.58, 0.05]# Merging across timepoints
FI_B1M <- Full_data_all %>%
dplyr::select("ID", "Group", "A_PRE_FI_total", "D_M1_FI_total")## Formatting table as needed
FI_B1M_long <- FI_B1M %>%
pivot_longer(cols = c(A_PRE_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_MEM_B1M <- lmer(FI_Score ~ Group * Time + (1|ID), data = FI_B1M_long, REML = TRUE)
summary(FI_MEM_B1M)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FI_Score ~ Group * Time + (1 | ID)
## Data: FI_B1M_long
##
## REML criterion at convergence: 2627.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39593 -0.49332 -0.00673 0.48809 2.50102
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 10.482 3.238
## Residual 5.891 2.427
## Number of obs: 489, groups: ID, 259
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.8600 0.5722 351.6690 17.230
## GroupB_Controls 0.1683 0.6942 351.6690 0.242
## GroupC_Intervention 0.6157 0.6974 351.6690 0.883
## TimeD_M1_FI_total -0.1205 0.5119 237.2371 -0.235
## GroupB_Controls:TimeD_M1_FI_total -0.5991 0.6203 237.0764 -0.966
## GroupC_Intervention:TimeD_M1_FI_total -0.9938 0.6226 236.9204 -1.596
## Pr(>|t|)
## (Intercept) <2e-16 ***
## GroupB_Controls 0.809
## GroupC_Intervention 0.378
## TimeD_M1_FI_total 0.814
## GroupB_Controls:TimeD_M1_FI_total 0.335
## GroupC_Intervention:TimeD_M1_FI_total 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrpB_C GrpC_I TD_M1_ GB_C:T
## GrpB_Cntrls -0.824
## GrpC_Intrvn -0.820 0.676
## TmD_M1_FI_t -0.402 0.332 0.330
## GB_C:TD_M1_ 0.332 -0.403 -0.272 -0.825
## GC_I:TD_M1_ 0.331 -0.273 -0.403 -0.822 0.678anova (FI_MEM_B1M)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 1.403 0.702 2 256.73 0.1191 0.887779
## Time 44.086 44.086 1 236.88 7.4834 0.006698 **
## Group:Time 15.160 7.580 2 236.79 1.2867 0.278109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjPlot::tab_model(FI_MEM_B1M,
pred.labels=c("Intercept", "Psychoeducation Control", "Mindset Intervention", "Time (1 Month)", "Psychoeducation Control x Time","Mindset Intervention x Time"),
dv.labels=c("Functional Impairment"),
string.std_ci = "std. 95% CI",
string.std = "std. Beta",
show.std = TRUE,
show.est = FALSE)| Functional Impairment | |||
|---|---|---|---|
| Predictors | std. Beta | std. 95% CI | p |
| Intercept | 0.01 | -0.27 – 0.29 | <0.001 |
| Psychoeducation Control | 0.04 | -0.29 – 0.38 | 0.809 |
| Mindset Intervention | 0.15 | -0.19 – 0.49 | 0.378 |
| Time (1 Month) | -0.03 | -0.28 – 0.22 | 0.814 |
| Psychoeducation Control x Time | -0.15 | -0.45 – 0.15 | 0.335 |
| Mindset Intervention x Time | -0.25 | -0.55 – 0.06 | 0.111 |
| Random Effects | |||
| σ2 | 5.89 | ||
| τ00 ID | 10.48 | ||
| ICC | 0.64 | ||
| N ID | 259 | ||
| Observations | 489 | ||
| Marginal R2 / Conditional R2 | 0.012 / 0.644 | ||
parameters::standardise_parameters(FI_MEM_B1M)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ------------------------------------------------------------------
## (Intercept) | 0.01 | [-0.27, 0.29]
## GroupB_Controls | 0.04 | [-0.29, 0.38]
## GroupC_Intervention | 0.15 | [-0.19, 0.49]
## TimeD_M1_FI_total | -0.03 | [-0.28, 0.22]
## GroupB_Controls:TimeD_M1_FI_total | -0.15 | [-0.45, 0.15]
## GroupC_Intervention:TimeD_M1_FI_total | -0.25 | [-0.55, 0.06]