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Multiwave validation sampling for error‐prone electronic health records

Author

Listed:
  • Bryan E. Shepherd
  • Kyunghee Han
  • Tong Chen
  • Aihua Bian
  • Shannon Pugh
  • Stephany N. Duda
  • Thomas Lumley
  • William J. Heerman
  • Pamela A. Shaw

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, multiwave, 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 multiwave 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 six 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.

Suggested Citation

  • Bryan E. Shepherd & Kyunghee Han & Tong Chen & Aihua Bian & Shannon Pugh & Stephany N. Duda & Thomas Lumley & William J. Heerman & Pamela A. Shaw, 2023. "Multiwave validation sampling for error‐prone electronic health records," Biometrics, The International Biometric Society, vol. 79(3), pages 2649-2663, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2649-2663
    DOI: 10.1111/biom.13713
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    References listed on IDEAS

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    1. Wright, Tommy, 2017. "Exact optimal sample allocation: More efficient than Neyman," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 50-57.
    2. J. F. Lawless, 2018. "Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 28-44, January.
    3. N. E. Breslow & N. Chatterjee, 1999. "Design and analysis of two‐phase studies with binary outcome applied to Wilms tumour prognosis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 457-468.
    4. Nicola Heslehurst & Rute Vieira & Zainab Akhter & Hayley Bailey & Emma Slack & Lem Ngongalah & Augustina Pemu & Judith Rankin, 2019. "The association between maternal body mass index and child obesity: A systematic review and meta-analysis," PLOS Medicine, Public Library of Science, vol. 16(6), pages 1-20, June.
    5. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    6. Thomas Lumley & Pamela A. Shaw & James Y. Dai, 2011. "Connections between Survey Calibration Estimators and Semiparametric Models for Incomplete Data," International Statistical Review, International Statistical Institute, vol. 79(2), pages 200-220, August.
    7. Peisong Han, 2016. "Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 246-260, March.
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