Debashis Ghosh, University of Washington
Marginal Regression Models for Recurrent and Terminal Events
A major complication in the analysis of recurrent events
data from medical studies is the presence of deaths. We propose
semiparametric proportional rates and means models for the marginal
mean number of recurrences, taking into account that death precludes
further events. It is shown that when there is no loss to follow-up
censoring, these models can be estimated using existing methods. Two
new estimation procedures are proposed when censoring because of loss
to follow-up exists. The first is based on inverse probability of
censored weighting techniques, while the second is based on modeling
survival. The asymptotic results of the estimators from these
procedures are derived. Goodness of fit techniques for these models
are considered. Some joint inference strategies for combining results
from the recurrent events and deaths are examined. The finite-sample
behavior of the proposed methodologies is assessed in simulation
studies. The techniques are then applied to data from a cancer
clinical trial.