A systematic approach to subgroup analyses in a smoking cessation trial

AbstractBackground: Traditional approaches to subgroup analyses that test each moderating factor as a separate hypothesis can lead to erroneous conclusions due to the problems of multiple comparisons, model misspecification, and multicollinearity. Objective: To demonstrate a novel, systematic approach to subgroup analyses that avoids these pitfalls. Methods: A Best Approximating Model (BAM) approach that identifies multiple moderators and estimates their simultaneous impact on treatment effect sizes was applied to a randomized, controlled, 11-week, double-blind efficacy trial on smoking cessation of adult smokers with attention-deficit/hyperactivity disorder (ADHD), randomized to either OROS-methylphenidate (n = 127) or placebo (n = 128), and treated with nicotine patch. Binary outcomes measures were prolonged smoking abstinence and point prevalence smoking abstinence. Results: Although the original clinical trial data analysis showed no treatment effect on smoking cessation, the BAM analysis showed signi...