A systematic approach to subgroup analyses in a smoking cessation trial

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.

To demonstrate a novel, systematic approach to subgroup analyses that avoids these pitfalls.

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.

Although the original clinical trial data analysis showed no treatment effect on smoking cessation, the BAM analysis showed significant subgroup effects for the primary outcome of prolonged smoking abstinence: (1) lifetime history of substance use disorders (adjusted odds ratio [AOR] 0.27; 95% confidence interval [CI] 0.10-0.74), and (2) more severe ADHD symptoms (baseline score >36; AOR 2.64; 95% CI 1.17-5.96). A significant subgroup effect was also shown for the secondary outcome of point prevalence smoking abstinence--age 18 to 29 years (AOR 0.23; 95% CI 0.07-0.76).

The BAM analysis resulted in different conclusions about subgroup effects compared to a hypothesis-driven approach. By examining moderator independence and avoiding multiple testing, BAMs have the potential to better identify and explain how treatment effects vary across subgroups in heterogeneous patient populations, thus providing better guidance to more effectively match individual patients with specific treatments.