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Identification of Worsening Subjects and Treatment Responders in Comparative Longitudinal Studies

Thursday, December 3, 2015 - 16:00
Yumi Kondo, PhD Candidate- UBC Statistics
Statistics Seminar
Room 4192, Earth Science Buildling, 2207 Main Mall

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 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 method with
10 multiple sclerosis (MS) trial datasets, and assess it by simulation

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. The simulation study shows that
incorporating multiple outcomes improves the responder identification