Journal article
Recall bias in population-based case-control studies of ovarian cancer and genital talcum powder use: potential impact and quantitative bias analysis
BMC medical research methodology, Forthcoming
23 Mar 2026
PMID: 41872762
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.
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Details
- Title
- Recall bias in population-based case-control studies of ovarian cancer and genital talcum powder use: potential impact and quantitative bias analysis
- Creators
- Diezhang Wu - University of Massachusetts AmherstIgor Burstyn (Corresponding Author) - Drexel UniversityWilliam J Thompson - Oldham CouncilJing Qian - University of Massachusetts AmherstKenneth A Mundt - University of Massachusetts Amherst
- Publication Details
- BMC medical research methodology, Forthcoming
- Publisher
- Springer Nature
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Other Identifier
- 991022171586404721