Power Calculations in Meta-Analysis

Anand Seth Co-Author
SK Patent Associates LLC
 
Nisha Sheshashayee Co-Author
 
Suyang Gao Co-Author
University of Cincinnati
 
Neelakshi Chatterjee Co-Author
 
Zhaochong Yu Co-Author
Division of Biostatistics and Bioinformatics, DEPHS, University of Cincinnati
 
Marepalli Rao First Author
University of Cincinnati
 
Marepalli Rao Presenting Author
University of Cincinnati
 
Wednesday, Aug 7: 8:50 AM - 9:05 AM
3150 
Contributed Papers 
Oregon Convention Center 
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 

Main Sponsor

Section on Statistical Computing