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Parameter estimation following an adaptive treatment selection trial design


  • Xiaolong Luo,

    Corresponding author
    1. Celgene Corporation, 86 Morris Avenue, Summit, NJ 07901, USA
    2. Department of Biostatistics, UMDNJ-School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854, USA
    • Phone: +1-908-673-2234, Fax: +1-908-673-2775
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  • Samuel S. Wu,

    1. Department of Biostatistics, University of Florida, Gainesville, FL 32610, USA
    2. Department of Statistics, National Cheng Kung University, Tainan, Taiwan 70101, Republic of China
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  • Jie Xiong

    1. Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
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Two-stage, drop-the-losers designs for adaptive treatment selection have been considered by many authors. The distributions of conditional sufficient statistics and the Rao–Blackwell technique were used to obtain an unbiased estimate and to construct an exact confidence interval for the parameter of interest. In this paper, we characterize the selection process from a binomial drop-the-losers design using a truncated binomial distribution. We propose a new estimator and show that it is asymptotically consistent with a large sample size in either the first stage or the second stage. Supported by simulation analyses, we recommend the new estimator over the naive estimator and the Rao–Blackwell-type estimator based on its robustness in the finite-sample setting. We frame the concept as a simple and easily implemented procedure for phase 2 oncology trial design that can be confirmatory in nature, and we use an example to illustrate its application.