High-dimensional randomization-based inference capitalizing on classical design and modern computing
Marie-Abele Bind,Donald B. Rubin +1 more
TL;DR: In this paper , a scalar summary statistic is used to evaluate the effect of exposure to ozone versus clean air on the DNA methylome, where the multivariate outcome involved 484,531 genomic locations and the proposed test yields a single null randomization distribution, and thus a single Fisher-exact p -value that is statistically valid regardless of the structure of the data.
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Abstract: Abstract A common complication that can arise with analyses of high-dimensional data is the repeated use of hypothesis tests. A second complication, especially with small samples, is the reliance on asymptotic p -values. Our proposed approach for addressing both complications uses a scientifically motivated scalar summary statistic, and although not entirely novel, seems rarely used. The method is illustrated using a crossover study of seventeen participants examining the effect of exposure to ozone versus clean air on the DNA methylome, where the multivariate outcome involved 484,531 genomic locations. Our proposed test yields a single null randomization distribution, and thus a single Fisher-exact p -value that is statistically valid whatever the structure of the data. However, the relevance and power of the resultant test requires the careful a priori selection of a single test statistic. The common practice using asymptotic p -values or meaningless thresholds for “significance” is inapposite in general.
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Introduction to the Vol. 50, No. 1, 2023
TL;DR: This study performed sensitivity analysis to assess the impact of the choice of prior dis-tributions in the three Bayesian inference methods and illustrates the proposed methods using real data from the MY-Health Study to explore racial/ethnic disparities in anxiety among cancer survivors.
Cox reduction and confidence sets of models: a theoretical elucidation
Robert Michael Lewis,Heather Battey +1 more
- 24 Feb 2023
TL;DR: In this paper , the authors identify features of the covariate matrix that may reduce its efficacy and evaluate possible reduction schemes based on penalised regression or marginal screening, before theoretically elucidating the reduction of [9].
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