alexa On Two-Stage Adaptive Seamless Design with Count Data from Different Study Durations under Weibull Distribution
ISSN: 2169-0138

Drug Designing: Open Access
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Research Article

On Two-Stage Adaptive Seamless Design with Count Data from Different Study Durations under Weibull Distribution

Qingshu Lu1, Shein Chung Chow2 and Siu Keung Tse3*
1Singapore Clinical Research Institute, Singapore
2Duke University School of Medicine, Durham, North Carolina, USA
3City University of Hong Kong, Republic of China
Corresponding Author : Siu Keung Tse
City University of Hong Kong
Republic of China
Tel: +852 34428578
E-mail: [email protected]
Received May 01, 2014; Accepted July 11, 2014; Published July 16, 2014
Citation: Lu Q, Chow SC, Tse SK (2014) On Two-Stage Adaptive Seamless Design with Count Data from Different Study Durations under Weibull Distribution. Drug Des 3:114. doi:10.4172/2169-0138.1000114
Copyright: © 2014 Lu Q, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

In clinical development, a two-stage design combining two separate studies (e.g., a phase II dose finding study and a phase III confirmatory study) into a single trial is commonly considered. The purpose of a two-stage design is not only to reduce lead time between the two studies, but also to evaluate the treatment effect in a more efficient way. In practice, one of the difficulties in utilizing a two-stage design is that the study endpoints at different stages may be different. For example, a biomarker (or the same study endpoint with different duration) may be considered at the first stage, while a regular study endpoint is used at the second stage. As per the studies the case where both study endpoints are continuous variables with certain correlation structure. In this paper, our attention is on the case where the study endpoints are count data which are obtained at the two stages with different time intervals. Statistical procedure for combining data observed from the two different stages are proposed. Furthermore, results on hypotheses testing and sample size calculation are derived for the comparison of two treatments based on data observed from a two-stage design.

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