  Electronic health record (EHR) data are increasingly used for biomedical
research, but these data have recognized data quality challenges. Data
validation is necessary to use EHR data with confidence, but limited resources
typically make complete data validation impossible. Using EHR data, we
illustrate prospective, multi-wave, two-phase validation sampling to estimate
the association between maternal weight gain during pregnancy and the risks of
her child developing obesity or asthma. The optimal validation sampling design
depends on the unknown efficient influence functions of regression coefficients
of interest. In the first wave of our multi-wave validation design, we estimate
the influence function using the unvalidated (phase 1) data to determine our
validation sample; then in subsequent waves, we re-estimate the influence
function using validated (phase 2) data and update our sampling. For
efficiency, estimation combines obesity and asthma sampling frames while
calibrating sampling weights using generalized raking. We validated 996 of
10,335 mother-child EHR dyads in 6 sampling waves. Estimated associations
between childhood obesity/asthma and maternal weight gain, as well as other
covariates, are compared to naive estimates that only use unvalidated data. In
some cases, estimates markedly differ, underscoring the importance of efficient
validation sampling to obtain accurate estimates incorporating validated data.
