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Identification of worsening subjects and treatment responders in comparative longitudinal studies

University of British Columbia

This thesis discusses the problems of identifying worsening individuals in on-going clinical trials and treatment responders in completed trials. We develop a new modelling approach to enhance a recently proposed method to detect increases of contrast enhancing lesions (CELs) on repeated magnetic resonance imaging, which have been used as an indicator for potential adverse events. The method signals patients with unusual increases in CEL activity by estimating the probability of observing CEL counts as large as those observed on a patient's recent scans conditional on the patient's CEL counts on previous scans. This index, computed based on a mixed effect negative binomial regression model, can vary substantially depending on the choice of distribution for the patient-specific random effects. Therefore, we relax this parametric assumption to model the random effects with an infinite mixture of beta distributions, using the Dirichlet process, which effectively allows any form of distribution. As our inference is in the Bayesian framework, we adopt a meta-analytic approach to develop an informative prior based on previous trials. This is particularly helpful at the early stages of a trial. We illustrate our enhanced method with 10 multiple sclerosis (MS) trial datasets, and assess it by simulation studies. Identification of treatment responders is a challenge in comparative studies where a treatment efficacy is measured by various longitudinally-collected continuous and count outcomes. Existing procedures often identify responders based on only a single outcome. We propose to classify patients according to their posterior probability of being a responder estimated based on a multiple outcome mixture model. Our novel model assumes that, conditioning on a cluster label, each longitudinal outcome is from the generalized linear mixed effect model (GLMM), arguably the most popular longitudinal model. As GLMM is a rich class of models, our general procedure enables finding responders comprehensively defined by multiple outcomes from various distributions. We utilize the Monte Carlo expectation-maximization algorithm to obtain the maximum likelihood estimates of our high-dimensional model. We demonstrate the generality of our procedure on two MS trial datasets. Our simulation study shows that incorporating multiple outcomes improves the responder identification performance.

Text

2016-04-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57447

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/