Bagged Empirical Null p-values: A Method to Account for Model Uncertainty in Large Scale Inference

TL;DR

This paper introduces bagged empirical null p-values (BEN p-values) that incorporate model uncertainty through bootstrap aggregation, improving large-scale inference accuracy and reproducibility in high-dimensional studies.

Contribution

The authors propose a novel method combining empirical null p-values with bootstrap aggregation to enhance inference robustness and reproducibility in large-scale testing.

Findings

BEN p-values often significantly change the ranking of significant findings.

Re-analysis of leukemia data revealed new findings supported by prior biological evidence.

Simulation studies demonstrate BEN p-values outperform existing methods in stability and accuracy.

Abstract

When conducting large scale inference, such as genome-wide association studies or image analysis, nominal $p$-values are often adjusted to improve control over the family-wise error rate (FWER). When the majority of tests are null, procedures controlling the False discovery rate (Fdr) can be improved by replacing the theoretical global null with its empirical estimate. However, these other adjustment procedures remain sensitive to the working model assumption. Here we propose two key ideas to improve inference in this space. First, we propose $p$-values that are standardized to the empirical null distribution (instead of the theoretical null). Second, we propose model averaging $p$-values by bootstrap aggregation (Bagging) to account for model uncertainty and selection procedures. The combination of these two key ideas yields bagged empirical null $p$-values (BEN $p$-values) that often dramatically alter the rank ordering of significant findings. Moreover, we find that a multidimensional selection criteria based on BEN $p$-values and bagged model fit statistics is more likely to yield reproducible findings. A re-analysis of the famous Golub Leukemia data is presented to illustrate these ideas. We uncovered new findings in these data, not detected previously, that are backed by published bench work pre-dating the Gloub experiment. A pseudo-simulation using the leukemia data is also presented to explore the stability of this approach under broader conditions, and illustrates the superiority of the BEN $p$-values compared to the other approaches.