The generalized drop-the-loser rule for treatment allocation in multi-treatment clinical studies.

• Other Authors: Sun, Ruibo.
• Format: Others
• Language: English; Chinese
• Published: 2006
• Subjects: Clinical trials; Clinical Trials as Topic; Random Allocation
• Online Access: http://library.cuhk.edu.hk/record=b6074109; http://repository.lib.cuhk.edu.hk/en/item/cuhk-343738

Complete randomized experimental design has been a popular standard in clinical trials due to its abilities to yield a smaller bias and to provide a strong foundation for statistical inferences. However, because of ethical issues, there is a growing demand for response adaptive designs which shift the allocation probabilities such that more patients receive the better treatment. The principal idea of response adaptive designs to allocate fewer patients to treatments which appears to be inferior based on the accruing response data and information collected up to the current stage. Although they are more complex than complete randomized experiments in theory and method, adaptive designs are practically more useful. The objective of this dissertation is to develop an adaptive design, the generalized drop-the-loser rule, for comparing multiple treatments in clinical trials, including the situation where the responses of patients are delayed moderately. Asymptotic properties including strong consistency and asymptotic normality of the new design are investigated through the martingale technique. Monte Carlo simulation studies are conducted to reveal the empirical performance of this design. The new design will be potentially useful in the practical context of clinical trials. === Sun Ruibo. === "July 2006." === Adviser: Siu Hung Cheung. === Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6488. === Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. === Includes bibliographical references (p. 62-66). === Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Abstracts in English and Chinese. === School code: 1307.