Semiparametric efficient estimation of survival distributions in two-stage randomisation designs in clinical trials with censored data
1 Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania 15261, U.S.A. wahed{at}pitt.edu, 2 Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, U.S.A. tsiatis{at}stat.ncsu.edu
Two-stage randomisation designs are useful in the evaluation of combination therapies where patients are initially randomised to an induction therapy and then, depending upon their response and consent, are randomised to a maintenance therapy. In this paper we derive the best regular asymptotically linear estimator for the survival distribution and related quantities of treatment regimes. We propose an estimator which is easily computable and is more efficient than existing estimators. Large-sample properties of the proposed estimator are derived and comparisons with other estimators are made using simulation.
Key Words: Censoring; Influence function; Inverse probability weighted estimator; Potential outcome; Regular asymptotically linear estimator; Two-stage design
Received June 2004. Revised August 2005.
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