Abdus Shakoor Wahed
Department of Statistics
North Carolina State University
Friday, February 28, 2003
10:00 A.M.
Mayo 100
Minneapolis Campus
Abstract:
Two-stage designs, where patients are initially randomized to an induction therapy
and then depending upon their response and consent, are randomized to a maintenance
therapy, are common in cancer and other clinical trials. The goal is to compare
different combinations of primary and maintenance therapies to find the combination
that is most beneficial. In practice, the analysis is usually conducted in two
separate stages which does not directly address the major objective of finding
the best combination. Recently Lunceford et. al. (2002, Biometrics, 58, 48-57)
introduced ad hoc estimators for the survival distribution and mean restricted
survival time under different treatment policies. These estimators are consistent
but not efficient, and do not include information from auxiliary covariates.
In this paper we derive estimators that are easy to compute and are more efficient
than previous estimators. We also show how to improve efficiency further by
taking into account additional information from auxiliary variables. Large sample
properties of these estimators are derived and comparisons with other estimators
are made using simulation. We apply our estimators to a leukemia clinical trial
data set that motivated this study.
Key words: Induction therapy; Influence functions; Intent-to-treat; Inverse probability weighted estimator; Missing data; Maintenance therapy; Counterfactuals or Potential outcomes; Survival analysis.