DESCRIPTION (Adapted from the Applicant's Abstract): The major purpose of this
research is to develop new methods for the design and analysis of time to event
data that are encountered in cancer clinical trials. The research will focus on
four topics.
1. Methods will be developed for jointly modeling and estimating the
relationship of longitudinally measured covariates to censored survival data
using a proportional hazards regression model.
2. A comprehensive approach will be developed for estimating and testing
relationships regarding a primary outcome variable that is missing on some
individuals due to incomplete follow-up.
3. Since the difficulty with incomplete follow-up is most pronounced during
interim monitoring, improper inferential procedures on the primary outcome can
severely bias stopping rules for early termination of a clinical trial. We will
show how to use the tests derived in topic (2) above to build group-sequential
stopping rules which have the appropriate operating characteristics.
4. We will show how multiple imputation can be used to estimate the parameters
and the standard error of these estimates in a proportional hazards model with
missing covariates.
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