Pooled analysis of nested case-control and case-cohort studies for risk assessment and prediction

Music City Center 

Pooled analyses across multiple cohort studies are increasingly common due to greater statistical power. However, in prospective biomarker studies, full cohort measurements of biomarkers of interest are often unavailable due to logistical and financial constraints, requiring nested case-control or case-cohort designs. While methods exist for pooling nested case-control samples, combining studies with different sampling designs remains a challenge. Motivated by the B2RISK consortium, which includes both designs, we propose methods for relative risk evaluation and risk prediction by pooling multiple studies with different designs. For relative risk evaluation, we use inverse probability weighting with a robust variance estimator for consistent estimation. For risk prediction, we employ pseudo-likelihood to incorporate parental cohort data from nested case-control studies, leading to consistent and efficient risk prediction rules. Through extensive simulations, we evaluate our methods' performance and demonstrate their advantages over standard approaches, including commonly used ad hoc methods in practice. We apply our methods to the B2RISK breast cancer study for illustration.

biomarker data

combined analysis

conditional likelihood

unconditional likelihood

logistic regression

selection bias 

Section on Statistics in Epidemiology