Choi, Jiin
(2012)
Prediction in the Joint Modeling of Mixed Types of Multivariate Longitudinal Outcomes and a Time-to-Event Outcome.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
A common goal of longitudinal studies is to relate a set of repeated observations to a time-to-event endpoint. One example of such a design is in the area of a late-life depression research where repeated measurement of cognitive and functional outcomes can contribute to one's ability to predict whether or not an individual will have a major depressive episode over a period of time. This research proposes a novel model for the relationship between multivariate longitudinal measurements and a time-to-event outcome. The goal of this model is to improve prediction for the time-to-event outcome by considering all longitudinal measurements simultaneously.
In this dissertation, we investigate a joint modeling approach for mixed types of multivariate longitudinal outcomes and a time-to-event outcome using a Bayesian paradigm. For the longitudinal model of continuous and binary outcomes, we formulate multivariate generalized linear mixed models with two types of random effects structures: shared random effects and correlated random effects. For the joint model, the longitudinal outcomes and the time-to-event outcome are assumed to be independent conditional on available covariates and the shared parameters, which are associated with the random effects of the longitudinal outcome processes. A Bayesian method using Markov chain Monte Carlo (MCMC) computed in OpenBUGS is implemented for parameter estimation.
We illustrate the prediction of future event probabilities within a fixed time interval for patients based on our joint model, utilizing baseline data, post-baseline longitudinal measurements, and the time-to-event outcome. Prediction of event or mortality probabilities allows one to intervene clinically when appropriate. Hence, such methods provide a useful public health tool at both the individual and the population levels.
The proposed joint model is applied to data sets on the maintenance therapies in a late-life depression study and the mortality in idiopathic pulmonary fibrosis. The performance of the method is also evaluated in extensive simulation studies.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
24 September 2012 |
| Date Type: |
Completion |
| Defense Date: |
8 August 2012 |
| Approval Date: |
24 September 2012 |
| Submission Date: |
20 July 2012 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
88 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Joint model; Mixed binary and continuous data; Multivariate longitudinal data; Prediction model; Shared parameter model; Survival analysis |
| Date Deposited: |
24 Sep 2012 18:55 |
| Last Modified: |
19 Dec 2016 14:38 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/13537 |
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Prediction in the Joint Modeling of Mixed Types of Multivariate Longitudinal Outcomes and a Time-to-Event Outcome. (deposited 24 Sep 2012 18:55)
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