Publication | Open Access
Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies
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References
2004
Year
Genetic TestingGenetic EpidemiologyEpidemiologic ResearchPopulation Health SciencesPathologyStatistical PowerBiological SignificanceClinical GeneticsNegative ResultBiostatisticsEpidemiologic MethodPublic HealthMolecular DiagnosticsMedical StatisticGeneral EpidemiologyVariant InterpretationPersonal GenomicsStatistical GeneticsCommon Cancer SitesMolecular Epidemiology StudiesEpidemiologyCancer EpidemiologyPositive ReportP ValueMedicine
Many reported genetic associations with common cancers and complex diseases are false positives, largely because significance is judged solely on P‑values below 0.05. The authors aim to demonstrate how to calculate the false positive report probability (FPRP) and use it to determine whether a statistically significant finding merits attention. FPRP is computed from the observed P‑value, the prior probability of a true association, and the test’s statistical power, and the authors illustrate this calculation to guide interpretation. Applying an FPRP‑based criterion improves study design, analysis, and interpretation, helping investigators, editors, and readers avoid overinterpreting significant results that are unlikely to reflect true associations.
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.
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