Data analysis
SECONDARY, REGRESSIONS
Descriptive statistics were reported as counts and percentages for categorical variables and means and standard deviations for continuous variables. Logistic regressions were performed to examine drop out at each time point. Caregivers who participated were dummy coded as 1 (retention) and those who dropped out were coded as 0 (drop out). Baseline characteristics were then entered as predictors of drop-out at each time-point. Results indicated unmarried, divorced or widowed caregivers were more likely to dropout at 2 months, and those who had reduced their working hours or stopped working were more likely to drop out at 4, 6 and 12 months (see Supplementary Table B). As a result these variables were adjusted for in the main analysis.Linear mixed models (PROC Mixed procedure), with an unstructured correlation matrix, were used to estimate between-group differences in the primary and secondary outcomes at each time point and to determine the predicted least-square (LS) mean values. Each model included group (intervention or control) as a fixed effect, and time as both a fixed and random effect. Each model was adjusted for marital status and change in employment status (fitted as fixed effects), and also included a time by group interaction.The effect of missing data was investigated as part of a sensitivity analysis using multiple imputation under the missing not at random (MNAR) assumption by searching for a tipping point that reverses the primary outcome conclusion [All analyses were performed using Statistical Analysis Software (SAS/STAT) (version 9.4; SAS Institute, Cary, NC).