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A Clustering Method to Identify Who Benefits Most from the Treatment Group in Clinical Trials.

Health Psychology and Behavioral Medicine 2014;2(1):723-734. [doi: 10.1080/21642850.2014.924857]

Beom S. Lee, PhD (University of South Florida), Pranab K. Sen, PhD, MSc (University of North Carolina), Nan S. Park, PhD (University of South Florida), Roger A. Boothroyd, PhD (University of South Florida), Roger H. Peters, PhD (University of South Florida), David A. Chiriboga, PhD (University of North Carolina).

In randomized controlled trials (RCTs), the most compelling need is to determine whether the treatment condition was more effective than the 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 reanalysis of a publicly available data set from the National Institute on Drug Abuse's National Drug Abuse Treatment Clinical Trials Network (protocol CTN-0004). That study examined the effectiveness of motivational enhancement therapy from 461 outpatients with substance use disorder problems. 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%).

Conclusions: The proposed method, LGEM, 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. LGEM has potential as a means of further exploring reasons why individuals respond to treatment conditions, regardless of which treatment arm they are exposed to, and can be implemented after the trial is completed, without need for a pre-specified design and can be used by any type of RCT in a variety of topic areas. (Article (Peer-Reviewed), PDF, English, 2014)

Keywords: CTN platform/ancillary study | Motivational Enhancement Therapy (MET) | Statistical analysis | Statistical models | Health Psychology and Behavioral Medicine (journal)

Document No: 1109.

Submitted by the CTN Dissemination Librarians, 12/12/2014.

AUTHORS SEARCH LINK
Boothroyd, Roger A. search
Chiriboga, David A. search
Lee, Beom S. search mail
Park, Nan S. search
Peters, Roger H. search
Sen, Pranab K. search
PROTOCOLS
NIDA-CTN-0004 search www

Supported by a grant from the National Institute on Drug Abuse to the University of Washington Alcohol and Drug Abuse Institute.
The materials on this site have neither been created nor reviewed by NIDA.
Updated 12/2014 -- http://ctndisseminationlibrary.org/display/1109.htm
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