Big data, observational research and P-value: a recipe for false-positive findings? A study of simulated and real prospective cohorts
| Autor: | Antonella Zambon, Piero Quatto, Giovanni Veronesi, Giordano Savelli, Guido Grassi |
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| Přispěvatelé: | Veronesi, G, Grassi, G, Savelli, G, Quatto, P, Zambon, A |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Epidemiology media_common.quotation_subject Population Context (language use) 030204 cardiovascular system & hematology survival analysis External validity 03 medical and health sciences 0302 clinical medicine cohort studies Bias big data Statistics Humans Medicine AcademicSubjects/MED00860 selection bias Computer Simulation Prospective Studies 030212 general & internal medicine Observational studies education calibration of P-value Aged media_common Selection bias education.field_of_study Proportional hazards model business.industry selection bia General Medicine Middle Aged Observational studie Nominal level Observational Studies as Topic Italy Data Interpretation Statistical SECS-S/01 - STATISTICA Female Observational study P Values business Type I and type II errors cohort studie |
| Zdroj: | International Journal of Epidemiology |
| Popis: | Background An increasing number of observational studies combine large sample sizes with low participation rates, which could lead to standard inference failing to control the false-discovery rate. We investigated if the ‘empirical calibration of P-value’ method (EPCV), reliant on negative controls, can preserve type I error in the context of survival analysis. Methods We used simulated cohort studies with 50% participation rate and two different selection bias mechanisms, and a real-life application on predictors of cancer mortality using data from four population-based cohorts in Northern Italy (n = 6976 men and women aged 25–74 years at baseline and 17 years of median follow-up). Results Type I error for the standard Cox model was above the 5% nominal level in 15 out of 16 simulated settings; for n = 10 000, the chances of a null association with hazard ratio = 1.05 having a P-value Conclusions In the analyses of large observational studies prone to selection bias, the use of empirical distribution to calibrate P-values can substantially reduce the number of trivial results needing further screening for relevance and external validity. |
| Databáze: | OpenAIRE |
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