Quantitative Bias Analysis of Talc and Ovarian Cancer

News & Events

March 21, 2024

Gradient’s recently published paper quantifies the potential impact of recall bias in talc and ovarian cancer epidemiology studies.

Recall bias is a familiar concept to epidemiologists. In case-control studies, people with (cases) and without (controls) a health condition are compared with respect to a potential risk factor. In these studies, recall bias can occur when cases and controls remember exposures, events, or experiences from the past differently. Recall bias can result in over- or underestimates of the true risk, though this bias is rarely quantified. The quantification of the potential impact of biases, including recall bias, in epidemiology studies has been getting more attention recently, and Gradient scientists conducted a case study in which they quantified recall bias; this case study is the subject of a newly published paper.

In this new study, the potential impact of recall bias on the results reported in case-control studies was evaluated in a quantitative recall bias analysis examining the relationship between talc exposure and ovarian cancer. Unlike cohort studies of talc exposure and ovarian cancer, which report no overall association, case-control studies have consistently observed small increased risks. Gradient used recently published data on the recall of talc use from the Sister Study, a National Institutes of Health (NIH) study of women with sisters with breast cancer, combined with data from the largest and most recent case-control study of talc and ovarian cancer, to simulate the impact of differential misclassification of talc-use recall on estimated risk estimates. They found that even a modest degree of recall bias could change the statistical significance of risk estimates. This work demonstrates how quantitative bias analyses can contribute to our understanding of disease risks.

Link to the article: “Quantitative recall bias analysis of the talc and ovarian cancer association

If you have any questions about this analysis, or its implications, please visit our website or contact:

Denali Boon, Ph.D., M.P.H.
Senior Epidemiologist

Julie Goodman, Ph.D., DABT, FACE, ATS