Paliwal, Yuvika
(2017)
Generalized linear mixed models for analysis of cross-correlated binary data in multi-reader studies of diagnostic imaging.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Cross-correlated data occur in multi-sample studies with a fully crossed design. An important type of binary cross-correlated data results from multi-reader diagnostic imaging studies where each of several readers independently evaluates the same sample of subjects for the presence or absence of a specific condition (e.g., disease).
The analysis of the fully crossed studies can be challenging because of the need to address both reader and subject variability and the related correlation structure. Generalized Linear Mixed Models (GLMM) are implemented in standard statistical software and offer a natural tool for the analysis of the cross-correlated data in the presence of covariates. However, performance of GLMMs for cross-correlated binary data from typical multi-reader studies is generally unknown and is questionable due to the specifics of the available estimation approaches.
In the first part of the dissertation we investigate the standard built-in GLMM methods for cross-correlated binary data with and without covariates and explore simple combinations of the built-in estimation techniques to overcome existing deficiencies. In the second part, we propose a half-marginal GLMM approach which offers a superior interpretation in the context of multi-reader studies of diagnostic accuracy. Our investigation of this model demonstrates good quality of statistical inferences in typical scenarios, but indicates possible large-sample problems stemming from the pseudo-likelihood estimation approach. In the third part of the dissertation we develop an explicit approach for estimating half-marginal model parameters without using pseudo-likelihood. The consistent fixed-effect estimator and its variance are evaluated in an extensive simulation study. The proposed approach can be implemented using the non-iterative combination of results from several robust Generalized Estimating Equation (GEE) models and, for simple scenarios, provides estimates that are equivalent to the empirical estimates.
Public Health Significance: Analyses of cross-correlated data from multi-reader studies are used to evaluate performance of medical diagnostic technologies at their development and regulatory approval stages. Enhanced methods of performance assessment help improve and accelerate optimal adaptation of diagnostic and screening technologies in clinical practice.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
29 June 2017 |
| Date Type: |
Publication |
| Defense Date: |
12 April 2017 |
| Approval Date: |
29 June 2017 |
| Submission Date: |
2 April 2017 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
140 |
| 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: |
GLMM, Cross-Correlated data |
| Date Deposited: |
29 Jun 2017 23:46 |
| Last Modified: |
01 May 2018 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31156 |
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