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Simultaneous Modeling of the Impact of Treatments on Alcohol Consumption and Quality of Life in the COMBINE Study: A Coupled Hidden Markov Analysis

Authors

  • James J. Prisciandaro,

    Corresponding author
    • Clinical Neuroscience Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina
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  • Stacia M. DeSantis,

    1. Division of Biostatistics and Epidemiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina
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  • Dipankar Bandyopadhyay

    1. Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota
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Reprint requests: James J. Prisciandaro, PhD, Clinical Neuroscience Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, 67 President Street, MSC861, Charleston, SC 29425; Tel.: 843-792-1433; Fax: 843-792-0528; E-mail: priscian@musc.edu

Abstract

Background

Clinical trials for alcoholism have historically regarded alcohol consumption as the primary outcome. In a subset of trials, quality of life (QOL) has been considered as a secondary outcome. Joint latent-variable modeling techniques may provide a more accurate and powerful simultaneous analysis of primary and secondary outcomes in clinical trials. The goal of this study was to evaluate longitudinal associations between treatment status, alcohol consumption, and QOL in the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study.

Methods

A total of 1,383 alcohol-dependent patients were randomized to 9 treatment groups. Percent heavy drinking days (PHDD) and health-related QOL from the 30 days preceding baseline, week 16, and week 52 were calculated using the Form 90 and the Medical Outcomes Study Health Survey Short Form-12 (SF-12), respectively. Latent profile analysis (LPA) was conducted to determine an appropriate number of latent states to represent PHDD and QOL. Subsequently, univariate and coupled hidden Markov models (for PHDD and SF-12 mental health, and PHDD and SF-12 physical) were fit to the data.

Results

LPA suggested that PHDD should be represented by 3 latent states and that each SF-12 scale should be represented by 2 states. Joint modeling results suggested that (i) naltrexone significantly predicted decreased PHDD (p < 0.05), and marginally predicted improved mental health QOL via decreased PHDD (p < 0.10), and (ii) that the combinations of naltrexone and combined behavioral intervention (CBI), and acamprosate and CBI, each predicted significantly improved physical QOL (p < 0.05), and marginally predicted decreased PHDD via improved physical QOL (p < 0.10).

Conclusions

This study illustrates a powerful and novel statistical approach for simultaneously evaluating the impact of treatments on primary and secondary outcomes in clinical trials. This study also suggests that behavioral interventions may impact drinking behavior through their ameliorative effects on QOL.

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