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SAT: a Surrogate-Assisted Two-wave case boosting sampling method, with application to EHR-based association studies.

Xiaokang Liu1, Jessica Chubak2,3, Rebecca A Hubbard1

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|December 28, 2021
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Summary
This summary is machine-generated.

This study introduces a new sampling method to improve the accuracy of electronic health record (EHR) studies. The surrogate-assisted two-wave (SAT) method enhances association estimation by selecting informative samples for phenotype validation.

Keywords:
association studyelectronic health recordserror in phenotyperare diseasesampling strategy

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Epidemiology

Background:

  • Electronic health records (EHRs) are valuable for studying phenotype-risk factor associations.
  • EHR-derived phenotypes can be error-prone, leading to bias.
  • Low prevalence phenotypes pose efficiency challenges in EHR studies.

Purpose of the Study:

  • To develop novel sampling methods addressing both bias from surrogate phenotypes and low efficiency in EHR studies.
  • To improve the accuracy of association estimation by selecting optimal subsamples for gold standard phenotype collection.

Main Methods:

  • Developed a surrogate-assisted two-wave (SAT) sampling method.
  • Employed surrogate-guided sampling (SGS) and modified optimal subsampling (OSMAC) sequentially.
  • Validated the method through simulations and an EHR dataset of breast cancer survivors.

Main Results:

  • The proposed SAT method selects informative observations, significantly reducing the mean squared error of association estimators.
  • The method effectively handles rare cases and misclassified surrogate phenotypes.

Conclusions:

  • The SAT approach improves the accuracy and efficiency of phenotype association studies using EHR data.
  • It successfully boosts case prevalence in subsamples, enhancing estimation accuracy, especially with well-behaved surrogates.