Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies

TL;DR: This commentary shows how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or "noteworthy" and shows how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies.

Abstract: Too many reports of associations between genetic variants and common cancer sites and other complex diseases are false positives. A major reason for this unfortunate situation is the strategy of declaring statistical significance based on a P value alone, particularly, any P value below.05. The false positive report probability (FPRP), the probability of no true association between a genetic variant and disease given a statistically significant finding, depends not only on the observed P value but also on both the prior probability that the association between the genetic variant and the disease is real and the statistical power of the test. In this commentary, we show how to assess the FPRP and how to use it to decide whether a finding is deserving of attention or "noteworthy." We show how this approach can lead to improvements in the design, analysis, and interpretation of molecular epidemiology studies. Our proposal can help investigators, editors, and readers of research articles to protect themselves from overinterpreting statistically significant findings that are not likely to signify a true association. An FPRP-based criterion for deciding whether to call a finding noteworthy formalizes the process already used informally by investigators--that is, tempering enthusiasm for remarkable study findings with considerations of plausibility.