{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T19:32:22Z","timestamp":1771356742454,"version":"3.50.1"},"reference-count":34,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2012,5,2]],"date-time":"2012-05-02T00:00:00Z","timestamp":1335916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Alcoholism Clin &amp; Exp Res"],"published-print":{"date-parts":[[2012,12]]},"abstract":"Background<\/jats:title>Clinical trials for alcoholism have historically regarded alcohol consumption as the primary outcome. In a subset of trials, quality of life (QOL<\/jats:styled-content>) has been considered as a secondary outcome. Joint latent\u2010variable 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<\/jats:styled-content> in the C<\/jats:styled-content>ombined P<\/jats:styled-content>harmacotherapies and B<\/jats:styled-content>ehavioral I<\/jats:styled-content>nterventions for A<\/jats:styled-content>lcohol D<\/jats:styled-content>ependence (COMBINE<\/jats:styled-content>) study.<\/jats:p><\/jats:sec>Methods<\/jats:title>A total of 1,383 alcohol\u2010dependent patients were randomized to 9 treatment groups. Percent heavy drinking days (PHDD<\/jats:styled-content>) and health\u2010related QOL<\/jats:styled-content> from the 30\u00a0days preceding baseline, week 16, and week 52 were calculated using the F<\/jats:styled-content>orm 90 and the M<\/jats:styled-content>edical O<\/jats:styled-content>utcomes S<\/jats:styled-content>tudy H<\/jats:styled-content>ealth S<\/jats:styled-content>urvey S<\/jats:styled-content>hort F<\/jats:styled-content>orm\u201012 (SF<\/jats:styled-content>\u201012), respectively. Latent profile analysis (LPA<\/jats:styled-content>) was conducted to determine an appropriate number of latent states to represent PHDD<\/jats:styled-content> and QOL<\/jats:styled-content>. Subsequently, univariate and coupled hidden M<\/jats:styled-content>arkov models (for PHDD<\/jats:styled-content> and SF<\/jats:styled-content>\u201012 mental health, and PHDD<\/jats:styled-content> and SF<\/jats:styled-content>\u201012 physical) were fit to the data.<\/jats:p><\/jats:sec>Results<\/jats:title>\nLPA<\/jats:styled-content> suggested that PHDD<\/jats:styled-content> should be represented by 3 latent states and that each SF<\/jats:styled-content>\u201012 scale should be represented by 2 states. Joint modeling results suggested that (i) naltrexone significantly predicted decreased PHDD<\/jats:styled-content> (p<\/jats:italic>\u00a0<\u00a00.05), and marginally predicted improved mental health QOL<\/jats:styled-content> via decreased PHDD<\/jats:styled-content> (p<\/jats:italic>\u00a0<\u00a00.10), and (ii) that the combinations of naltrexone and combined behavioral intervention (CBI<\/jats:styled-content>), and acamprosate and CBI<\/jats:styled-content>, each predicted significantly improved physical QOL<\/jats:styled-content> (p<\/jats:italic>\u00a0<\u00a00.05), and marginally predicted decreased PHDD<\/jats:styled-content> via improved physical QOL<\/jats:styled-content> (p<\/jats:italic>\u00a0<\u00a00.10).<\/jats:p><\/jats:sec>Conclusions<\/jats:title>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<\/jats:styled-content>.<\/jats:p><\/jats:sec>","DOI":"10.1111\/j.1530-0277.2012.01823.x","type":"journal-article","created":{"date-parts":[[2012,5,2]],"date-time":"2012-05-02T18:12:53Z","timestamp":1335982373000},"page":"2141-2149","source":"Crossref","is-referenced-by-count":6,"title":["Simultaneous Modeling of the Impact of Treatments on Alcohol Consumption and Quality of Life in the COMBINE<\/scp> Study: A Coupled Hidden Markov Analysis"],"prefix":"10.1111","volume":"36","author":[{"given":"James J.","family":"Prisciandaro","sequence":"first","affiliation":[{"name":"Clinical Neuroscience Division, Department of Psychiatry and Behavioral Sciences Medical University of South Carolina Charleston South Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stacia M.","family":"DeSantis","sequence":"additional","affiliation":[{"name":"Division of Biostatistics and Epidemiology, Department of Medicine Medical University of South Carolina Charleston South Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dipankar","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Division of Biostatistics University of Minnesota School of Public Health Minneapolis Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,5,2]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.295.17.2003"},{"key":"e_1_2_6_3_1","unstructured":"BrandM OliverN PentlandA(1997)Coupled Hidden Markov models for complex action recognition. 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