Abstract
Oftentimes, we want to discover the genes whose expression levels are associated with a time-to-event endpoint, such as progression free survival or overall survival, through microarray studies. In this case, we need to adjust the false positivity in such discovery procedure for multiplicity of the genes using a multiple testing method. The most popular multiple testing methods used for gene discovery in microarray studies are to control the false discovery rate or the family wise error rate. In this paper, we review a FDR-control method to discover the genes associated with a time-to-event outcome and propose a sample size calculation method for microarray studies designed to discover genes whose expression levels are associated the chosen time-to-event outcome. These methods can be easily modified for other types of high throughput genome projects.
Highlights
Microarray has been a major high-throughput assay method to display DNA or RNA abundance for a large number of genes concurrently
One of the main objectives for conducting microarray studies is to discover the genes whose expression levels are associated with a time-to-event outcome, such as time to progression or survival
One important aspect in gene discovery using a microarray study is to adjust the false positivity for multiplicity of the genes
Summary
Microarray has been a major high-throughput assay method to display DNA or RNA abundance for a large number of genes concurrently. Discovery of the prognostic genes should be made taking multiplicity into account, and with enough statistical power to discover important genes successfully. There are two types of false positivity we control in multiple testing for gene discovery with microarray data. Dobbin and Simon present analytical formulae for determining the number of biological replicates needed for developing a predictive classifier [4] None of these publications provides a comprehensive investigation of sample size estimation for discovery of genes whose expression levels are associated with a survival outcome. We propose sample size and power calculation methods for microarray studies to discover genes using Cox’s regression method while controlling the FDR and for sample size calculation for gene discovery with a FWER control [6,7]
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