Analysis
REGRESSION, SECONDARY, SENSITIVITY
Trial data from all randomized participants were collected via secure web-based case record forms and stored using REDCap. Baseline characteristics and outcome data were first summarized descriptively: continuous variables were summarized as mean and standard deviation (SD), and categorical variables as frequency and percentage.Analysis was conducted according to a modified intention-to-treat (ITT) principle, to include all randomized participants in the first two waves. For the main ITT analysis, we used multiple imputations for missing primary outcome data. Sensitivity analysis was also considered using the baseline value carried forward (BVCF) approach on missing data to test the robustness of the main results. Per protocol (PP) analysis was also performed on randomized participants who provided primary outcome data with no major protocol violations. Linear regression models were used to evaluate the effect of the intervention on change in body weight at 52 weeks (primary outcome), adjusting for baseline body weight, age, study wave, and stratification factors (BMI category, self-reported ethnicity, study center). Given the COVID-19 related issues, we were unable to undertake sub-group analysis by ethnicity.Similar regression models were used on continuous secondary outcomes at 12- and 52-weeks. Model-adjusted mean differences were reported with 95% confidence intervals (CIs). Logistic regression models were used on the adherence to healthy lifestyle behaviors outcomes at 12 and 52 weeks. Adjusted odds ratios (ORs) were reported with 95% CIs. For secondary outcomes, no imputation was considered on missing outcome data. Confidence interval widths have not been adjusted for multiplicity and may not be used in place of hypothesis testing. Statistical analysis was performed using SAS version 9.4 (SAS Institute Inc.). All statistical tests were two-sided with a 5% significance level.