randomized controlled trial, treatment effectiveness, cluster analysis, substance abuse, motivational enhancement therapy
Digital Object Identifier (DOI)
In randomized controlled trials (RCTs), the most compelling need is to determine whether the treatment condition was more effective than control. However, it is generally recognized that not all participants in the treatment group of most clinical trials benefit equally. While subgroup analyses are often used to compare treatment effectiveness across pre-determined subgroups categorized by patient characteristics, methods to empirically identify naturally occurring clusters of persons who benefit most from the treatment group have rarely been implemented. This article provides a modeling framework to accomplish this important task. Utilizing information about individuals from the treatment group who had poor outcomes, the present study proposes an a priori clustering strategy that classifies the individuals with initially good outcomes in the treatment group into: (a) group GE (good outcome, effective), the latent subgroup of individuals for whom the treatment is likely to be effective and (b) group GI (good outcome, ineffective), the latent subgroup of individuals for whom the treatment is not likely to be effective. The method is illustrated through a re-analysis of a publically available data set from the National Institute on Drug Abuse. The RCT examines the effectiveness of motivational enhancement therapy from 461 outpatients with substance abuse problems. The proposed method identified latent subgroups GE and GI, and the comparison between the two groups revealed several significantly different and informative characteristics even though both subgroups had good outcomes during the immediate post-therapy period. As a diagnostic means utilizing out-of-sample forecasting performance, the present study compared the relapse rates during the long-term follow-up period for the two subgroups. As expected, group GI, composed of individuals for whom the treatment was hypothesized to be ineffective, had a significantly higher relapse rate than group GE (63% vs. 27%; χ2 = 9.99, p-value = .002).
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Citation / Publisher Attribution
Health Psychology and Behavioral Medicine, v. 2, issue 1, p. 723-734
Scholar Commons Citation
Lee, Beom S.; Sen, Pranab K.; Park, Nan; Boothroyd, Roger A.; Peters, Roger H.; and Chiriboga, David A., "A Clustering Method to Identify who Benefits Most from the Treatment Group in Clinical Trials" (2014). Mental Health Law & Policy Faculty Publications. 942.