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Bayesian Analysis of Latent Trait Hierarchical Models for Multiple Binary Outcomes in Cluster Randomized Clinical Trials

Zhao, Xinhua (2011) Bayesian Analysis of Latent Trait Hierarchical Models for Multiple Binary Outcomes in Cluster Randomized Clinical Trials. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

In clinical trials, multiple endpoints for treatment efficacy often are obtained, and in addition, data may be collected hierarchically. Statistical analyses become very challenging for this multidimensional hierarchical data, particularly with data collected at more than two levels. We propose a latent variable approach to assess an intervention effect on multiple binary outcomes from three-level hierarchical data. This approach incorporates the correlation structure into one or more latent outcomes, and simultaneously regresses the latent outcome(s) on observed covariates. Random effects are included to model the hierarchical structure. Parameters estimation is done using a fully Bayesian approach implemented in WinBUGS. We first illustrate the approach in a cluster randomized clinical trial of three interventions to improve the processes of care for outpatients with pneumonia. Four binary outcomes are collected at the patient-level and clustered at the provider and clinic site levels. Simulation studies are conducted to check the algorithm and computational implementation. Then, we extend the one latent trait model to a two-latent trait model using eight outcomes from both outpatient and inpatient care. This latent modeling approach provides a comprehensive way to analyze multivariate hierarchical data. The method not only allows assessment of intervention effects with respect to multiple outcomes, but also assesses the relationship between outcomes, identifies those outcomes that carry the most information about the latent trait(s), and provides a summary measure of the "quality of care" at each clinical site.This work extends existing methods to model multivariate binary endpoints in a cluster-randomized clinical trial. The public health significance of this study is the potential usefulness of this approach to quantify intervention (or exposure) effects with regard to multiple outcomes in hierarchical data setting, which arises frequently in medical and epidemiologic studies.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhao, Xinhuaxhzhao26@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairStone, Roslyn Aroslyn@pitt.eduROSLYN
Committee MemberYe, Feifeifeifeiye@pitt.eduFEIFEIYE
Committee MemberRockette, Howard Eherbst@pitt.eduHERBST
Committee MemberFine, Michael Jfinemj@upmc.edu
Committee MemberMazumdar, Satimaz1@pitt.eduMAZ1
Date: 31 January 2011
Date Type: Completion
Defense Date: 15 September 2010
Approval Date: 31 January 2011
Submission Date: 1 December 2010
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: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian approach; hierarchical models; latent variable models
Other ID: http://etd.library.pitt.edu/ETD/available/etd-12012010-024803/, etd-12012010-024803
Date Deposited: 10 Nov 2011 20:07
Last Modified: 15 Nov 2016 13:52
URI: http://d-scholarship.pitt.edu/id/eprint/9934

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