HIV Working Group

Emma Davies Smith, PhD
Postdoctoral Research Fellow, Harvard T.H. Chan School of Public Health, Department of Biostatistics

Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints
Abstract: When interventions have wide-ranging impacts, cluster randomized trials may employ multiple endpoints. When a single summary of effects across endpoints is of primary interest, global hypothesis testing/effect estimation methods represent a common analysis strategy. However, specification of the joint distribution required by these methods is non-trivial, particularly when endpoint properties differ. This talk will discuss how these challenges can be addressed by considering a nonparametric global treatment effect, referred to as “the global win probability,” or the probability that a treatment individual responds better than a control individual on average. Estimation procedures replace each set of individual-level responses with a single, rank-based “global win fraction,” a directly interpretable quantity applicable to almost any set of endpoints. Linear mixed models are then directly applied to the global win fractions to recover point, variance, and interval estimates of the global win probability adjusted for clustering. Simulation results and a case study demonstrate the viability of the proposed methods. Possible extensions of the novel win fraction regression framework are also briefly discussed.