A systematic approach to subgroup analyses in a smoking cessation trial

Abstract

Background: 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 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). Conclusions: 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.

• Attention deficit hyperactivity disorder
• methylphenidate
• modeling
• statistics
• subgroup analysis
• tobacco

Funding

This research was made possible by grants from the National Institute on Drug Abuse Clinical Trials Network (U10-DA013732, PI: E. Somoza); National Institute of General Medical Sciences (NIGMS) (R43GM106465, PI: S.S. Henley), National Cancer Institute (NCI) (R44CA139607, PI: S.S. Henley) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (R43AA014302, PI: S.S. Henley; R43AA013670, PI: S.S. Henley; R43/44AA013351, PI: S.S. Henley; R43/44AA011607, PI: S.S. Henley) under the Small Business Innovation Research (SBIR) program; National Institute on Drug Abuse (5K08DA031245, PI: A.N. Westover). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper.