Yale University
Department of Statistics
Mixture Models for Longitudinal Outcomes which Depend on Event Times
Monday, October 21, 1996
Joseph Hogan
Department of Statistics
Brown University
Seminar to be held in Room 309, LEPH, 60 College
Many long-term studies collect both a vector of repeated measurements and
an event time on each subject; often, the two outcomes are dependent. One
example is the use of surrogate markers to predict disease onset or
survival. Another is a longitudinal clinical trial which has
outcome-related dropout. We describe a mixture model for the joint
distribution which accommodates incomplete repeated measures and
right-censored event times, and provide methods for maximum likelihood
estimation. Two applications are outlined: one is a randomized clinical
trial of a new therapy for schizophrenia in which poor responders are
systematically removed. The other is a pediatric AIDS trial in which an
intention to treat analysis is recovered from incomplete data.