A clustering method to identify who benefits most from the treatment group in clinical trials.
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 hadpooroutcomes, the present study proposes an a priori clustering strategy that classifies the individuals with initiallygoodoutcomes 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 hadgoodoutcomes 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).
Department of Mental Health Law & Policy, Louis de la Parte Florida Mental Health Institute, University of South Florida , Tampa , FL , USA.
Beom S
BS
Lee
Department of Biostatistics, University of North Carolina , Chapel Hill , NC , USA ; Department of Statistics and Operations Research, University of North Carolina , Chapel Hill , NC , USA.
Pranab K
PK
Sen
School of Social Work, University of South Florida , Tampa , FL , USA.
Nan S
NS
Park
Department of Mental Health Law & Policy, Louis de la Parte Florida Mental Health Institute, University of South Florida , Tampa , FL , USA.
Roger A
RA
Boothroyd
Department of Mental Health Law & Policy, Louis de la Parte Florida Mental Health Institute, University of South Florida , Tampa , FL , USA.
Roger H
RH
Peters
Department of Child & Family Studies, Louis de la Parte Florida Mental Health Institute, University of South Florida , Tampa , FL , USA.
David A
DA
Chiriboga
cluster analysis; motivational enhancement therapy; randomized controlled trial; substance abuse; treatment effectiveness