Representative Individualized Absolute Risk Estimation by Using Data from Epidemiologic Studies, Surveys, and Registries: Estimating Risks for Minority Subgroups

Absolute risks are fundamental to clinical decision-making but are often estimated from non-representative epidemiologic studies, which usually underrepresent minorities. "Model-based" methods use population registries to improve externally validity of absolute risk estimation but assume hazard ratios (HR) are transportable between data sources. "Pseudoweighting" methods improve representativeness of studies by using and external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification or inefficient due to variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that poststratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates to produce efficient and robust absolute risk estimation. For developing an all-cause mortality risk model representative for the US, our findings suggest that HRs for minorities are not transportable, and that surveys can have inadequate numbers of events for minorities. Poststratification on event rates is crucial for obtaining reliable estimates for minority subgroups.