Statistical analysis
ICH, weight reduction
REGRESSION, EVENTS, SECONDARY
A sample size of 600 participants provided power of >90% to demonstrate the superiority of tirzepatide MTD to placebo, for the coprimary endpoints, each at a two-sided significance level of 0.05. Sample size calculation assumed a difference of at least 12% in mean per cent weight reduction from randomization to week 72 for tirzepatide MTD as compared with placebo, a common s.d. of 10% and a dropout rate of 25%. Efficacy and safety endpoints were analyzed with data from all randomly assigned participants who took at least one dose of study drug (modified intention-to-treat population).Two estimands (TRE and efficacy) were used to assess treatment efficacy from different perspectives and accounted for intercurrent events differently.The TRE uses a treatment policy strategy to handle intercurrent events (ICH E9(R1)) and is intended to give an estimation of the average treatment effect of tirzepatide relative to placebo for all participants who had undergone randomization, regardless of treatment adherence. For estimation for this estimand, the intercurrent events and resulting missing values were handled by a hybrid approach using retrieved dropouts imputation from the same treatment group or using all nonmissing data assuming missing at random. This estimand is therefore also referred to as a ‘hybrid’ estimand in the study protocol. Continuous endpoints were analyzed using an analysis of covariance model, and categorical endpoints were analyzed by logistical regression. Both models included randomized treatment and stratification factors (country/pooled country, sex and per cent body weight reduction at the end of lead-in (<10 and ≥10%) as fixed effects, and baseline measure as a covariate. Analyses were conducted with hybrid imputation of missing body weight at 72 weeks and statistical inference over hybrid imputation of missing data guided by RubinSpecifically, for missing data solely due to COVID-19, missing data were considered as missing at random and imputed using all available nonmissing data of the outcome measurement from the same treatment arm; for missing data due to other intercurrent events, these were imputed based on retrieved dropouts in the same treatment arm, defined as observed primary outcome measurements, from participants in the same treatment group, who had their efficacy assessed after early discontinuation of the study drug.The efficacy estimand uses a hypothetical strategy to handle intercurrent events (ICH E9(R1)) and represented the average treatment effect of tirzepatide relative to placebo, before treatment discontinuation, for all participants who had undergone randomization. The resulting missing values (unobserved, discarded) after treatment discontinuation were implicitly handled using a mixed model for repeated measures (MMRM) under the assumption of missing at random. Continuous endpoints were analyzed using a MMRM model, and categorical endpoints by logistical regression. MMRM analysis included randomized treatment, visit, treatment-by-visit interaction and stratification factors (country/pooled country, sex and per cent body weight reduction at the end of lead-in (<10 and ≥10%) as fixed effects, and baseline measure as a covariate. The logistical regression model included randomized treatment, the same stratification factors as fixed effects and baseline measure as a covariate. Missing values were imputed by the predicted value from the MMRM model above, and continuous measurements were then dichotomized to binary outcomes. The type 1 error rate was controlled at a level of 0.05 within each estimand for evaluation of primary and key secondary objectives.Statistical analyses were carried out using SAS v.9.4, unless otherwise specified.