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Post hoc power estimation in large-scale multiple testing problems.

Sonja Zehetmayer¹, Martin Posch

¹Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.

Bioinformatics (Oxford, England)

|March 2, 2010

Summary

This summary is machine-generated.

Estimating the multiple Type II error rate post hoc is crucial for large-scale multiple testing. This study evaluates post hoc estimators, finding those using empirical null distributions perform best with correlated data.

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Area of Science:

Biostatistics
Statistical Genetics
Bioinformatics

Background:

Assessing statistical power and Type II error rates in large-scale multiple testing is challenging due to unknown parameters.
Post hoc estimation of the multiple Type II error rate using observed data is a viable alternative.
Gene expression microarray experiments exemplify complex multiple testing scenarios.

Purpose of the Study:

To evaluate a class of post hoc estimators for the multiple Type II error rate.
To investigate the statistical properties of these estimators derived from the proportion of true null hypotheses.
To compare estimator performance across various distributional scenarios.

Main Methods:

Considered post hoc estimators as functions of the estimated proportion of true null hypotheses.
Conducted an extensive simulation study to assess the statistical properties of derived estimators.
Investigated estimators based on empirical distributions of null hypotheses.

Main Results:

Estimator performance, measured by mean squared error, is highly dependent on the distributional scenario.
Estimators utilizing empirical null distributions demonstrated superior performance when test statistics were strongly correlated.
The choice of estimator significantly impacts accuracy in large-scale hypothesis testing.

Conclusions:

Post hoc estimation methods offer a practical approach to assessing Type II error rates in complex statistical problems.
Empirical distribution-based estimators are recommended for analyses involving correlated test statistics.
The study provides valuable insights for selecting appropriate methods in multiple testing scenarios.