Growth Modeling with Nonignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

Autor/inn/en	Muthen, Bengt; Asparouhov, Tihomir; Hunter, Aimee M.; Leuchter, Andrew F.
Titel	*Growth Modeling with Nonignorable Dropout: Alternative Analyses of the STARD Antidepressant Trial**
Quelle	In: Psychological Methods, 16 (2011) 1, S.17-33 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
DOI	10.1037/a0022634
Schlagwörter	Structural Equation Models; Depression (Psychology); Trend Analysis; Drug Therapy; Longitudinal Studies; Outcomes of Treatment; Attrition (Research Studies); Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Trendanalyse; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework with the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling. A new selection model not only allows an influence of the outcomes on missingness but allows this influence to vary across classes. Model selection is discussed. The missing data models are applied to longitudinal data from the Sequenced Treatment Alternatives to Relieve Depression (STARD) study, the largest antidepressant clinical trial in the United States to date. Despite the importance of this trial, STARD growth model analyses using nonignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. (Contains 17 figures and 7 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10