Bayesian additional evidence for decision making under small sample uncertainty.

ABSTRACT:

Background

Statistical inference based on small datasets, commonly found in precision oncology, is subject to low power and high uncertainty. In these settings, drawing strong conclusions about future research utility is difficult when using standard inferential measures. It is therefore important to better quantify the uncertainty associated with both significant and non-significant results based on small sample sizes.

Methods

We developed a new method, Bayesian Additional Evidence (BAE), that determines (1) how much additional supportive evidence is needed for a non-significant result to reach Bayesian posterior credibility, or (2) how much additional opposing evidence is needed to render a significant result non-credible. Although based in Bayesian analysis, a prior distr

SUBMITTER: Sondhi A 

PROVIDER: S-EPMC8543928 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature