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Yanyuan Ma, University of South Carolina

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Robust mixed-effects model for clustered failure time data: application to Huntington's disease
14 January 2016 from 4:00 PM to 5:00 PM
201 Thomas Building
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Yanyuan Ma

An important goal in clinical and statistical research is estimating the distribution for clustered failure times, which have a natural intra-class dependency and are subject to censoring. We propose to handle these inherent challenges with a novel approach that does not impose restrictive modeling or distributional assumptions. Rather, using a logit transformation, we relate the distribution for clustered failure times to covariates and a random, subject-specific effect such that the covariates are modeled with unknown functional forms, and the random effect is distribution-free and potentially correlated with the covariates. Over a range of time points, the model is shown to be reminiscent of an additive logistic mixed effect model. Such a structure allows us to handle censoring via pseudo-value regression and to develop semiparametric techniques that completely factor out the unknown random effect. We show both theoretically and empirically that the resulting estimator is consistent for any choice of random effect distribution and for any dependency structure between the random effect and covariates. Lastly, we illustrate the method's utility in an application to the Cooperative Huntington’s Observational Research Trial data, where our method provides new insights into differences between motor and cognitive impairment event times in subjects at risk for Huntington's disease.


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