Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients.
Identification of treatment responders is a challenge in comparative studies where treatment efficacy is measured by multiple longitudinally collected continuous and count outcomes. Existing procedures often identify responders on the basis of only a single outcome. We propose a novel multiple longitudinal outcome mixture model that assumes that, conditionally on a cluster label, each longitudinal outcome is from a generalized linear mixed effect model. We utilize a Monte Carlo expectation-maximization algorithm to obtain the maximum likelihood estimates of our high-dimensional model and classify patients according to their estimated posterior probability of being a responder. We demonstrate the flexibility of our novel procedure on two multiple sclerosis clinical trial datasets with distinct data structures. Our simulation study shows that incorporating multiple outcomes improves the responder identification performance; this can occur even if some of the outcomes are ineffective. Our general procedure facilitates the identification of responders who are comprehensively defined by multiple outcomes from various distributions. Copyright © 2017 John Wiley & Sons, Ltd.
Robert Bosch LLC Research and Technology Center North America, Data Mining Services and Solutions, Palo Alto, California, U.S.A.
Yumi
Y
Kondo
BC Centre for Improved Cardiovascular Health, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
Yinshan
Y
Zhao
Department of Statistics, University of British Columbia, Vancouver, BC, Canada.
John
J
Petkau
D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D009103:Multiple Sclerosis / Q000188:drug therapy*; D016896:Treatment Outcome
Monte Carlo expectation-maximization algorithm; generalized linear mixed effect model; longitudinal data; mixture model; responders in clinical trials