Abstract Number:
3150
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Marepalli Rao (1), Anand Seth (2), Nisha Sheshashayee (3), Suyang Gao (1), Neelakshi Chatterjee (3), Zhaochong Yu (4)
Institutions:
(1) University of Cincinnati, N/A, (2) SK Patent Associates LLC, United States, (3) N/A, N/A, (4) Division of Biostatistics and Bioinformatics, DEPHS, University of Cincinnati, N/A
Co-Author(s):
Zhaochong Yu
Division of Biostatistics and Bioinformatics, DEPHS, University of Cincinnati
First Author:
Presenting Author:
Abstract Text:
Power calculations in the environment of testing hypotheses are well hashed out. To unleash these calculations, we need 1. A family of models; 2. A null hypothesis; 3. The alternative hypothesis; 4. Data; 5. A test statistic T 6. The distribution of T under the null and alternative hypotheses; 7. Alpha; 9. A test. Power calculations let us choose a test and sample size. The main goal of this presentation is to bring entire modus operandi into the realm of Meta-Analysis. The overarching purpose of Meta-Analysis is to synthesize several studies all focusing on the same testing problem. Let m be the number of studies chosen for synthesis. Information on the studies is collected in two ways. 1. Relevant summary statistics from each study. 2. P-values from each study. There are studies, which give only p-values. These studies are the focus of this presentation. There are scores of tests proposed and used in the literature on synthesis. Tippett's test, Fisher's test, Pearson's test are some examples. We initiate power calculations as a function m of the number of studies on these tests. We show how power calculations help us to make comparisons between the tests.
Keywords:
Power|Sample Size|Meta-Analysis|Tippett test|Fisher test|Number of Studies for Meta-Analysis
Sponsors:
Section on Statistical Computing
Tracks:
Miscellaneous
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