Analyzing binary longitudinal data in adaptive clinical trials

Bari, Wasimul (2003) Analyzing binary longitudinal data in adaptive clinical trials. Masters thesis, Memorial University of Newfoundland.

[English] PDF (Migrated (PDF/A Conversion) from original format: (application/pdf)) - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (2MB) [English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. (Original Version)

Abstract

In an adaptive clinical trial research, it is common to use certain data dependent design weights to assign individuals to treatments so that more study subjects are assigned to the better treatment. These design weights must also be used for consistent estimation of the treatment effects as well as the effects of other prognostic factors. In practice, there are however situations where it may be necessary to collect binary responses repeatedly from an individual over a period of time and to obtain consistent and efficient estimates for the treatment effects as well as the effects of the other covariates. In this thesis, we introduce a binary response based longitudinal adaptive design for the allocation of individuals to a better treatment, and propose a weighted generalized quasi-likelihood (WGQL) approach for the consistent and efficient estimation of the regression parameters, including the treatment effects. We also introduce a binary longitudinal adaptive mixed model assuming that given the treatment effects and the unobservable individual random effect, repeated responses of an individual are longitudinally correlated. An extended WGQL approach is also used to obtain consistent and efficient estimators for the regression parameters and the variance component of individual random effects.

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