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.
Original language | English (US) |
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Pages (from-to) | 4906-4923 |
Number of pages | 18 |
Journal | Statistics in Medicine |
Volume | 32 |
Issue number | 28 |
DOIs | |
State | Published - Dec 10 2013 |
Keywords
- Effect modification
- Heterogeneity of treatment effect
- Interaction
- Risk stratification
- Subgroup analysis
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability