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Biostatistics Dissertation Defenses

Biostatistics Dissertations


Events that appear on this page are in the Sponsoring Department of "Biostatistics" with "Dissertation Defenses" as its Category.

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Wed 12/6/2017 12:00PM - 2:00PM
Song Zhang: Diagnostic Accuracy Analysis for Ordinal Competing Risks Outcomes Using ROC Surface
Public Health 7139

Song Zhang of the Department of Biostatistics defends her dissertation on "Diagnostic Accuracy Analysis for Ordinal Competing Risks Outcomes Using ROC Surface".

Fri 12/8/2017 11:30AM - 1:30PM
Yongli Shuai: Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored...
Public Health 7139

Yongli Shuai of the Department of Biostatistics defends his dissertation on "Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored Competing Risks Data".


Biostatistics Dissertation Defense

Yuvika Paliwal: "Generalized Linear Mixed Models for Analysis of Cross-correlated Binary Data in..."

Thursday 4/13 10:00AM - 12:00PM
Public Health 4140
Yuvika Paliwal of the Department of Biostatistics defends her dissertation on "Generalized Linear Mixed Models for Analysis of Cross-correlated Binary Data in Multi-reader Studies of Diagnostic Imaging"

Graduate faculty of the University and all other interested parties are invited to attend.

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). Such studies are used during development, optimization and regulatory approval of medical technologies, which can eventually be implemented for diagnostics and screening in clinical practice. 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 in 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 built-in estimation techniques to overcome existing deficiencies. In the second part, we propose a half-marginal GLMM approach which offers 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 a simulation study. The proposed approach can be implemented using non-iterative combination of results from robust GEE models and, for simple scenarios, provides estimates that are equivalent to the empirical estimates.

Last Updated On Friday, July 07, 2017 by Valenti, Renee Nerozzi
Created On Tuesday, March 14, 2017

Questions and Submissions

The schedule of dissertation defenses in the Department of Biostatistics is maintained by:

Renee Valenti


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