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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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Jiang_et_al-2018-Statistics_in_Medicine.pdf Restricted Access | Published version | 626.78 kB | Adobe PDF | View/Open Request a copy |
524c.pdf | Peer-reviewed author version | 282.71 kB | Adobe PDF | View/Open |
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