Abstract Number:
2803
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Gonzalo Contador (1)
Institutions:
(1) Universidad Tecnica Federico Santa Maria, Santiago, Chile
First Author:
Presenting Author:
Abstract Text:
Combining p-values in meta-analysis is a popular method when test data are unavailable or challenging to merge into a global significance. A variety of methods with different statistical properties exist in the continuous case (when the null distribution of the p-value is uniform). Heard and Delanchy (2018) reframed each method as a likelihood ratio test, guiding the selection of a most powerful combiner for a specific alternative. Discrete p-values present additional challenges, as their null distribution varies significantly, making the distribution of each combiner intractable. We first present a testing framework based on a Wasserstein-closest modification of a p-value towards a target distribution, show that under very mild conditions it produces asymptotically consistent tests. We present the closed form approximation statistics for common methods (Fisher, Pearson, Edgington, Stouffer, George) and presenting the optimal choice of a most powerful discrete combiner in many alternative hypothesis settings, presenting some applications in public health, weak and sparse signal detection, and genetic and genomic association tests.
Keywords:
p-value combination|Meta-Analysis|Stouffer's Method|Edgington’s method|Fisher’s method|George’s method
Sponsors:
Section on Statistical Computing
Tracks:
Data Science
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