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SHARED PARAMETER METHOD FOR MODELING THE EVOLUTION OF DEPRESSIVE SYMPTOMS IN LONGITUDINAL STUDIES WITH NONIGNORABLE MISSING DATA

Yang, Hsiao-Ching (2007) SHARED PARAMETER METHOD FOR MODELING THE EVOLUTION OF DEPRESSIVE SYMPTOMS IN LONGITUDINAL STUDIES WITH NONIGNORABLE MISSING DATA. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

In longitudinal studies of depressive symptoms in elderly patients, analyses are complicated by the presence of nonignorable missing data. In this study, we used data from the Monongahela Valley Independent Elders Survey (MoVIES) of 1,260 rural and elderly residents in western Pennsylvania. The method we used to analyze the evolution of depression is the shared parameter model, which is one of the methods that provide a framework for jointly modeling the longitudinal outcomes and the dropout process through a common frailty or unobserved random effects. When we used 2 different shared parameter models instead of using an unadjusted longitudinal model, we found the following decreases in the ratio of the odds of depression: a 2% decrease for women versus men (OR decreased from 2.05 in the unadjusted model to 2.00 in each shared parameter model); a 3% decrease for individuals with less than a high school education versus individuals with more than or equal to a high school education (OR decreased from 0.33 to 0.32); a 3% decrease for individuals taking fewer than 4 prescription drugs versus individuals taking 4 or more prescription drugs (OR decreased from 0.29 to 0.28); a 5% decrease for individuals using antidepressant drugs versus individuals not using antidepressant drugs (OR decreased from 16.15 to 15.35 in the first shared parameter model and to 15.39 in the second shared parameter model); and a 1% decrease for individuals with functional impairment versus individuals without functional impairment (OR decreased from 4.72 to 4.66 in the first shared parameter model and to 4.67 in the second shared parameter model). Because differences of this magnitude are likely to have an impact on decisions concerning public health policies and funding, it is important to take nonignorable missing data into account when analyzing longitudinal studies. Shared parameter models can be computationally demanding, so their performance should be judged by their goodness of fit and required running time.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Yang, Hsiao-Chingmaureen.yang@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChang, Chung-Chou Hochangj@pitt.eduCHANGJ
Committee MemberGanguli, MaryGanguliM@upmc.eduGANGULIM
Committee MemberArena, Vincent Carena@pitt.eduARENA
Date: 27 September 2007
Date Type: Completion
Defense Date: 24 July 2007
Approval Date: 27 September 2007
Submission Date: 28 July 2007
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: longitudinal; non-ignorable missing; shared parameter
Other ID: http://etd.library.pitt.edu/ETD/available/etd-07282007-152040/, etd-07282007-152040
Date Deposited: 10 Nov 2011 19:54
Last Modified: 19 Dec 2016 14:36
URI: http://d-scholarship.pitt.edu/id/eprint/8681

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