Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies

The multiplicity problem has become increasingly important in genetic studies as the capacity for high-throughput genotyping has increased. The control of False Discovery Rate (FDR) (Benjamini and Hochberg. [1995] J. R. Stat. Soc. Ser. B 57:289-300) has been adopted to address the problems of false positive control and low power inherent in high-volume genome-wide linkage and association studies. In many genetic studies, there is often a natural stratification of the m hypotheses to be tested. Given the FDR framework and the presence of such stratification, we investigate the performance of a stratified false discovery control approach (i.e. control or estimate FDR separately for each stratum) and compare it to the aggregated method (i.e. consider all hypotheses in a single stratum). Under the fixed rejection region framework (i.e. reject all hypotheses with unadjusted p-values less than a pre-specified level and then estimate FDR), we demonstrate that the aggregated FDR is a weighted average of the stratum-specific FDRs. Under the fixed FDR framework (i.e. reject as many hypotheses as possible and meanwhile control FDR at a pre-specified level), we specify a condition necessary for the expected total number of true positives under the stratified FDR method to be equal to or greater than that obtained from the aggregated FDR method. Application to a recent Genome-Wide Association (GWA) study by Maraganore et al. ([2005] Am. J. Hum. Genet. 77:685-693) illustrates the potential advantages of control or estimation of FDR by stratum. Our analyses also show that controlling FDR at a low rate, e.g. 5% or 10%, may not be feasible for some GWA studies.     

Statistical properties of a variance components method for quantitative trait linkage analysis in nuclear families and extended pedigrees

We investigated the statistical properties of a variance components method for quantitative trait linkage analysis using nuclear families and extended pedigrees. © 1997 Wiley-Liss, Inc.     

A method for testing a prespecified subgroup in clinical trials

In clinical trials, investigators are often interested in the effect of a given study treatment on a subgroup of patients with certain clinical or biological attributes in addition to its effect on the overall study population. Such a subgroup analysis would become even more important to the study sponsor if an efficacy claim can be made for the subgroup when the test for the overall study population fails at a prespecified alpha level. In practice, such a claim is often dependent on prespecification of the subgroup and certain implicit or explicit requirements placed on the study results due to ethical or regulatory concerns. By carefully considering these requirements, we propose a general statistical methodology for testing both the overall and subgroup hypotheses, which has optimal power and strongly controls the familywise Type I error rate.     

A comparison of goodness of fit tests for the logistic GEE model

Generalized estimating equations have become a popular regression method for analysing clustered binary data. Methods to assess the goodness of fit of the fitted models have recently been developed. However, evaluations and comparisons of these methods are limited. We discuss these methods and develop two additional statistics to evaluate goodness of fit. We evaluate the performance of each of the statistics with respect to type I error rates and power in a simulation study. Guidance is provided regarding appropriate use of the statistics under various scenarios.