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Full-Text Articles in Life Sciences

Power In Pairs: Assessing The Statistical Value Of Paired Samples In Tests For Differential Expression, John R. Stevens, Jennifer S. Herrick, Roger K. Wolff, Martha L. Slattery Dec 2018

Power In Pairs: Assessing The Statistical Value Of Paired Samples In Tests For Differential Expression, John R. Stevens, Jennifer S. Herrick, Roger K. Wolff, Martha L. Slattery

Mathematics and Statistics Faculty Publications

Background: When genomics researchers design a high-throughput study to test for differential expression, some biological systems and research questions provide opportunities to use paired samples from subjects, and researchers can plan for a certain proportion of subjects to have paired samples. We consider the effect of this paired samples proportion on the statistical power of the study, using characteristics of both count (RNA-Seq) and continuous (microarray) expression data from a colorectal cancer study.

Results: We demonstrate that a higher proportion of subjects with paired samples yields higher statistical power, for various total numbers of samples, and for various strengths of …


Pooling Of Variances: The Skeleton In The Mixed Model Closet?, Philip M. Dixon May 2018

Pooling Of Variances: The Skeleton In The Mixed Model Closet?, Philip M. Dixon

Conference on Applied Statistics in Agriculture and Natural Resources

I explore three related issues concerning pooling of error variances: when is it appropriate (or not) to pool, how best to evaluate equality of variances, and whether there is a cost to never pooling. I focus on pooling decisions in a combined analysis of a multi-site experiment. A-priori, sites should have different error variances. My primary question is whether an analysis that ignores unequal variances is wrong.

I find that ignoring heteroscedasticity between sites maintains, or provides slightly conservative, tests of average treatment effects and treatment-by-site interactions. Models with site-specific variances do provide more powerful tests when variances are different. …