Estimation following selection of the largest of two normal means

被引:32
作者
Stallard, Nigel [1 ]
Todd, Susan [2 ]
Whitehead, John [2 ]
机构
[1] Univ Warwick, Warwick Med Sch, Coventry CV4 7AL, W Midlands, England
[2] Univ Reading, Med & Pharmaceut Stat Res Unit, Reading RG6 2AH, Berks, England
关键词
clinical trial analysis; select and test designs; treatment selection;
D O I
10.1016/j.jspi.2007.05.045
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This paper considers the problem of estimation when one of a number of populations, assumed normal with known common variance, is selected on the basis of it having the largest observed mean. Conditional on selection of the population, the observed mean is a biased estimate of the true mean. This problem arises in the analysis of clinical trials in which selection is made between a number of experimental treatments that are compared with each other either with or without an additional control treatment. Attempts to obtain approximately unbiased estimates in this setting have been proposed by Shen [2001. An improved method of evaluating drug effect in a multiple dose clinical trial. Statist. Medicine 20, 1913-1929] and Stallard and Todd [2005. Point estimates and confidence regions for sequential trials involving selection. J. Statist. Plann. Inference 135, 402-419]. This paper explores the problem in the simple setting in which two experimental treatments are compared in a single analysis. It is shown that in this case the estimate of Stallard and Todd is the maximum-likelihood estimate (m.l.e.), and this is compared with the estimate proposed by Shen. In particular, it is shown that the m.l.e. has infinite expectation whatever the true value of the mean being estimated. We show that there is no conditionally unbiased estimator, and propose a new family of approximately conditionally unbiased estimators, comparing these with the estimators suggested by Shen. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:1629 / 1638
页数:10
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