Analysis of Error-prone Electronic Health Records with Multi-wave Validation Sampling: Association of Maternal Weight Gain during Pregnancy with Childhood Outcomes

Abstract

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 naïve 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.

Choosing good subsamples for regression modelling

“…However, if wave I is too large, the number sampled in some strata in wave I may already be larger than the number required by the optimal design, leading to sub-optimality, so that appropriate choice of wave I size is important. Shepherd et al (2021) studied maternal and child risk factors for childhood obesity and asthma in 10,335 children, using electronic health record data augmented by a Phase II sample of 996 manually audited records. A six-wave sampling design was used to choose a validation sample for gestational age, weight gain during pregnancy, and asthma status.…”

“…Performing the validation on a well-selected subsample gives the benefits of chart review at lower cost. Recently, Shepherd et al (2021) studied the association of maternal weight gain during pregnancy with child risk factors using the EHR data. A subsample of records, which are selected by the optimal design for design-based analysis, is validated by manual chart review.…”