  Representative risk estimation is fundamental to clinical decision-making.
However, risks are often estimated from non-representative epidemiologic
studies, which usually underrepresent minorities. "Model-based" methods use
population registries to improve externally validity of risk estimation but
assume hazard ratios (HR) are generalizable from samples to the target finite
population. "Pseudoweighting" methods improve representativeness of studies by
using an 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
pure risk estimation (i.e., a cause-specific absolute risk in the absence of
competing events). For developing an all-cause mortality risk model
representative for the US, our findings suggest that HRs for minorities are not
generalizable, and that surveys can have inadequate numbers of events for
minorities. Poststratification on event rates is crucial for obtaining reliable
risk estimation for minority subgroups.
