Abstract
INTRODUCTION: Quantitative bias analysis (QBA) methods have been developed, with growing applications to epidemiological studies. Bayesian QBA (BQBA) methods may be superior in effectively using evidence-based priors and study data to displace fixed-value assumptions, and in characterizing distributions of adjusted point estimates and associated uncertainty. METHODS: We applied simple QBA (SQBA) and BQBA methods to population-based case-control studies on ovarian cancer and genital talcum powder use. Our hypothesis was that differential misclassification of self-reported historical talcum powder use led to statistically significant associations in several of these studies. SQBA results covering ranges of published exposure sensitivity and specificity estimates for genital talcum powder use were generated. BQBA models generated posterior means and 95% credible intervals (CrI’s) of bias-adjusted odds ratios (ORs), sensitivities and specificities of controls and cases, respectively, using pooled case-control study data. RESULTS: The mean Bayesian posterior OR was 1.08 (95% CrI 0.67–1.77), mean case and control sensitivities were 0.87 and 0.78 and mean case and control specificities were 0.86 and 0.90, respectively. Model diagnostics indicated good fit. CONCLUSION: Upon adjusting only for hypothesized differential exposure misclassification bias, the association between ovarian cancer and genital talcum powder use was attenuated relative to the unadjusted one. While other potential sources of bias may be operating within this body of studies, exposure reporting bias likely accounts for the statistically significant ORs reported in the individual case-control studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-026-02836-x.