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Grant Details

Grant Number: 5R03CA150077-02 Interpret this number
Primary Investigator: Zhang, Jiajia
Organization: University Of South Carolina At Columbia
Project Title: Sample Size Method and Software Development in Survival Trial with a Cure Rate
Fiscal Year: 2011


Abstract

DESCRIPTION (provided by applicant): Abstract One of the crucial issues in designing randomized controlled clinical trials is to calculate sample size needed in the study, which is more complicated in survival trial. The existing method and software for the sample size calculation in survival analysis only consider the survival trial without a cure rate. To the best of our knowledge, no existing method and software can provide the power and sample size calculation when there is a cure rate in the treatment. In order to fill in this gap, we propose the statistically sound method for the sample size calculation in the mixture cure model, which include the existing sample size method based on the proportional hazards model as a special case, and we will develop the user friendly software in R during this grant period with a complete user manual. Therefore, the proposed method will provide a flexible tool for investigators to explore the optimal sample size in survival trial. The user friendly software solves the computational issue in the sample size calculation, and thus the researchers and practitioners can apply the proposed method in their study easily. PUBLIC HEALTH RELEVANCE: Project Narrative This project will provide a flexible tool for investigators to explore the optimal sample size in survival trial with or without a cure rate, and the user friendly software solves the computational issues in the sample size calculation from the proposed method. Thus the researchers and practitioners can apply the proposed method in their study easily.



Publications

Sample size calculation for the proportional hazards cure model.
Authors: Wang S. , Zhang J. , Lu W. .
Source: Statistics in medicine, 2012-12-20; 31(29), p. 3959-71.
EPub date: 2012-07-11.
PMID: 22786805
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