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Causality and PsychopathologyFinding the Determinants of Disorders and their Cures$
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Patrick Shrout, Katherine Keyes, and Katherine Ornstein

Print publication date: 2011

Print ISBN-13: 9780199754649

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199754649.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 08 December 2021

General Approaches to Analysis of Course: Applying Growth Mixture Modeling to Randomized Trials of Depression Medication

General Approaches to Analysis of Course: Applying Growth Mixture Modeling to Randomized Trials of Depression Medication

Chapter:
7 (p.159) General Approaches to Analysis of Course: Applying Growth Mixture Modeling to Randomized Trials of Depression Medication
Source:
Causality and Psychopathology
Author(s):

Bengt Muthé N

Hendricks C. Brown

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199754649.003.0012

This chapter discusses the assessment of treatment effects in longitudinal randomized trials using growth mixture modeling (GMM) (Muthén & Shedden, 1999; Muthén & Muthén, 2000; Muthén et al., 2002; Muthén & Asparouhov, 2009). GMM is a generalization of conventional repeated measurement mixed-effects (multilevel) modeling. It captures unobserved subject heterogeneity in trajectories not only by random effects but also by latent classes corresponding to qualitatively different types of trajectories. It can be seen as a combination of conventional mixed-effects modeling and cluster analysis, also allowing prediction of class membership and estimation of each individual’s most likely class membership. GMM has particularly strong potential for analyses of randomized trials because it responds to the need to investigate for whom a treatment is effective by allowing for different treatment effects in different trajectory classes. The chapter is motivated by a University of California, Los Angeles study of depression medication (Leuchter, Cook, Witte, Morgan, & Abrams, 2002). Data on 94 subjects are drawn from a combination of three studies carried out with the same design, using three different types of medications: fluoxetine (n = 14), venlafaxine IR (n = 17), and venlafaxine XR (n = 18). Subjects were measured at baseline and again after a 1-week placebo lead-in phase. In the subsequent double-blind phase of the study, the subjects were randomized into medication (n = 49) and placebo (n = 45) groups. After randomization, subjects were measured at nine occasions: at 48 hours and at weeks 1–8. The current analyses consider the Hamilton Depression Rating Scale. Several predictors of course of the Hamilton scale trajectory are available, including gender, treatment history, and a baseline measure of central cordance hypothesized to influence tendency to respond to treatment. The results of studies of this kind are often characterized in terms of an end point analysis where the outcome at the end of the study, here at 8 weeks, is considered for the placebo group and for the medication group.

Keywords:   Depression, Expectation-maximization algorithm, Hamilton Depression Rating Scale, Missing data, Placebo response, Responder class

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