title: Assessing heterogeneity of treatment effect in a clinical trial with the proportional interactions model.
abstract: Understanding how individuals vary in their response to treatment is an important task of clinical research. For standard regression models, a proportional interactions model first described by Follmann and Proschan (1999) offers a powerful approach for identifying effect modification in a randomized clinical trial when multiple variables influence treatment response. In this paper, we present a framework for using the proportional interactions model in the context of a parallel-arm clinical trial with multiple prespecified candidate effect modifiers. To protect against model misspecification, we propose a selection strategy that considers all possible proportional interactions models. We develop a modified Bonferroni correction for multiple testing that accounts for the positive correlation among candidate models. We describe methods for constructing a confidence interval for the proportionality parameter. In simulation studies, we show that our modified Bonferroni adjustment controls familywise error and has greater power to detect proportional interactions compared with multiplcity-corrected subgroup analyses. We demonstrate our methodology by using the Studies of Left Ventricular Dysfunction Treatment trial, a placebo-controlled randomized clinical trial of the efficacy of enalapril to reduce the risk of death or hospitalization in chronic heart failure patients. An R package called anoint is available for implementing the proportional interactions methodology.
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, U.S.A.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Kovalchik
authors.forename: Ravi
authors.initials: R
authors.lastname: Varadhan
authors.forename: Carlos O
authors.initials: CO
authors.lastname: Weiss
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D004656:Enalapril / Q000494:pharmacology / Q000627:therapeutic use; D006333:Heart Failure / Q000517:prevention & control; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000188:drug therapy
chemical_list: D004656:Enalapril
keywords: effect modification; heterogeneity of treatment effect; interaction; risk stratification; subgroup analysis