title: Assessing the probability that a positive report is false: an approach for 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.
authors.affiliation: Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-7244, USA. wacholder@nih.gov
authors.forename: Sholom
authors.initials: S
authors.lastname: Wacholder
authors.forename: Stephen
authors.initials: S
authors.lastname: Chanock
authors.forename: Montserrat
authors.initials: M
authors.lastname: Garcia-Closas
authors.forename: Laure
authors.initials: L
authors.lastname: El Ghormli
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Rothman
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D016001:Confidence Intervals; D015986:Confounding Factors, Epidemiologic; D005189:False Positive Reactions; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D008432:Mathematical Computing; D017720:Molecular Epidemiology / Q000379:methods*; D015588:Observer Variation; D016017:Odds Ratio; D018570:Risk Assessment / Q000379:methods*; D018401:Sample Size