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• W2299044786 abstract "Using prior information may improve power in frequentist multiple testing. P-value weighting is a promising methodology where each test is conducted at a different level, using critical values based on independent prior data. However, existing methods are limited, and do not allow the user to specify properties of the weights that are desired in practice, such as boundedness, or monotonicity in the strength of prior evidence. Here we develop a general framework for p-value weighting based on convex optimization. This allows flexible constraints and leads to a variety of new methods, such as bounded and monotone weights, stratified weights, and smooth weights. It also recovers several existing heuristics. Finally we focus on the promising special case of bounded monotone weights. These are appealing as they increase with the strength of prior evidence, and stable because they are within pre-specified bounds. We show they have good empirical power in the analysis of genome-wide association studies, better than any weighting method we have seen." @default.
• W2299044786 created "2016-06-24" @default.
• W2299044786 creator A5031235093 @default.
• W2299044786 date "2016-03-17" @default.
• W2299044786 modified "2023-09-27" @default.
• W2299044786 title "A general convex framework for multiple testing with prior information" @default.
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