Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26158
Title: Joint modeling of multiple ordinal adherence outcomes via generalized estimating equations with flexible correlation structure
Authors: Jiang, Zhen
Liu, Yimeng
Wahed, Abdus S.
MOLENBERGHS, Geert 
Issue Date: 2018
Source: STATISTICS IN MEDICINE, 37(6), p. 983-995
Abstract: Adherence to medication is critical in achieving effectiveness of many treatments. Factors that influence adherence behavior have been the subject of many clinical studies. Analyzing adherence is complicated because it is often measured on multiple drugs over a period, resulting in a multivariate longitudinal outcome. This paper is motivated by the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C study, where adherence is measured on two drugs as a bivariate ordinal longitudinal outcome. To analyze such outcome, we propose a joint model assuming the multivariate ordinal outcome arose from a partitioned latent multivariate normal process. We also provide a flexible multilevel association structure covering both between and within outcome correlation. In simulation studies, we show that the joint model provides unbiased estimators for regression parameters, which are more efficient than those obtained through fitting separate model for each outcome. The joint method also yields unbiased estimators for the correlation parameters when the correlation structure is correctly specified. Finally, we analyze the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C adherence data and discuss the findings.
Notes: Jiang, Z (reprint author), Univ Pittsburgh, Grad Sch Publ Hlth, Dept Biostat, Pittsburgh, PA 15261 USA. zhen.jiang@fda.hhs.gov
Keywords: adherence; generalized estimating equations; joint model; latent variable model; multivariate ordinal longitudinal data
Document URI: http://hdl.handle.net/1942/26158
ISSN: 0277-6715
e-ISSN: 1097-0258
DOI: 10.1002/sim.7560
ISI #: 000424293000008
Rights: Copyright © 2017 John Wiley & Sons, Ltd
Category: A1
Type: Journal Contribution
Validations: ecoom 2019
Appears in Collections:Research publications

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