< Back to previous page

Publication

Joint modeling of multiple ordinal adherence outcomes via generalized estimating equations with flexible correlation structure

Journal Contribution - Journal Article

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.
Journal: STATISTICS IN MEDICINE
ISSN: 0277-6715
Issue: 6
Volume: 37
Pages: 983 - 995
Publication year:2018
Keywords:adherence, generalized estimating equations, joint model, latent variable model, multivariate ordinal longitudinal data
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:1
CSS-citation score:1
Authors:International
Authors from:Higher Education
Accessibility:Open