Marie Davidian, Ph.D.

Professor of Statistics

North Carolina State University

Semiparametric Approaches for Inference in Joint Models

for Longitudinal and Time-to-Event Data

A common objective in longitudinal studies is to characterize the relationship between

a longitudinal response process and a time to event. Considerable recent interest has

focused on so-called joint models, where models for the event time distribution (typically

proportional hazards) and longitudinal data are taken to depend on a common set of latent

random effects, which are usually assumed to follow a multivariate normal distribution.

A natural concern is sensitivity to violation of this assumption. I will review the rationale

for and development of joint models and discuss two approaches to modeling and

inference that require no or only mild assumptions on the random effects distribution.

In this sense, the models and methods are “semiparametric.” The methods will be

demonstrated by application to data from an HIV clinical trial.