Analysis of an incomplete binary outcome dichotomized from an underlying continuous variable in clinical trials

Triple hormone receptor agonist retatrutide for metabolic dysfunction-associated steatotic liver disease: a randomized phase 2a trial

Retatrutide is a novel triple agonist of the glucose-dependent insulinotropic polypeptide, glucagon-like peptide 1 and glucagon receptors. A 48-week phase 2 obesity study demonstrated weight reductions of 22.8% and 24.2% with retatrutide 8 and 12 mg, respectively. The primary objective of this substudy was to assess mean relative change from baseline in liver fat (LF) at 24 weeks in participants from that study with metabolic dysfunction-associated steatotic liver disease and ≥10% of LF. Here, in this randomized, double-blind, placebo-controlled trial, participants (n = 98) were randomly assigned to 48 weeks of once-weekly subcutaneous retatrutide (1, 4, 8 or 12 mg dose) or placebo. The mean relative change from baseline in LF at 24 weeks was -42.9% (1 mg), -57.0% (4 mg), -81.4% (8 mg), -82.4% (12 mg) and +0.3% (placebo) (all P < 0.001 versus placebo). At 24 weeks, normal LF (<5%) was achieved by 27% (1 mg), 52% (4 mg), 79% (8 mg), 86% (12 mg) and 0% (placebo) of participants. LF reductions were significantly related to changes in body weight, abdominal fat and metabolic measures associated with improved insulin sensitivity and lipid metabolism. The ClinicalTrials.gov registration is NCT04881760 .

Return-to-baseline multiple imputation for missing values in clinical trials

Return-to-baseline is an important method to impute missing values or unobserved potential outcomes when certain hypothetical strategies are used to handle intercurrent events in clinical trials. Current return-to-baseline approaches seen in literature and in practice inflate the variability of the "complete" dataset after imputation and lead to biased mean estimators when the probability of missingness depends on the observed baseline and/or postbaseline intermediate outcomes. In this article, we first provide a set of criteria a return-to-baseline imputation method should satisfy. Under this framework, we propose a novel return-to-baseline imputation method. Simulations show the completed data after the new imputation approach have the proper distribution, and the estimators based on the new imputation method outperform the traditional method in terms of both bias and variance, when missingness depends on the observed values. The new method can be implemented easily with the existing multiple imputation procedures in commonly used statistical packages.