Minimum Hellinger distance estimation for randomized play the winner design

详细信息    查看全文

• 作者：An-lin Cheng and Anand N. Vidyashankar
• 关键词：Break-down point ; Clinical trial ; Hellinger distance ; Influence function ; Large deviations ; Monte-Carlo approximation ; Multi-type branching processes ; Response adaptive randomization
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2006
• 出版时间：1 June 2006
• 年：2006
• 卷：136
• 期：6
• 页码：1875-1910
• 全文大小：411 K

文摘

Response-adaptive designs in clinical trials incorporate information from prior patient responses in order to assign better performing treatments to the future patients of a clinical study. An example of a response adaptive design that has received much attention in recent years is the randomized play the winner design (RPWD). Beran [1977. Minimum Hellinger distance estimates for parametric models. Ann. Statist. 5, 445–463] investigated the problem of minimum Hellinger distance procedure (MHDP) for continuous data and showed that minimum Hellinger distance estimator (MHDE) of a finite dimensional parameter is as efficient as the MLE (maximum likelihood estimator) under a true model assumption. This paper develops minimum Hellinger distance methodology for data generated using RPWD. A new algorithm using the Monte Carlo approximation to the estimating equation is proposed. Consistency and asymptotic normality of the estimators are established and the robustness and small sample performance of the estimators are illustrated using simulations. The methodology when applied to the clinical trial data conducted by Eli-Lilly and Company, brings out the treatment effect in one of the strata using the frequentist techniques compared to the Bayesian argument of Tamura et al [1994. A case study of an adaptive clinical trialin the treatment of out-patients with depressive disorder. J. Amer. Statist. Assoc. 89, 768–776].