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Cited by 35Advanced Causal Inference TechniquesMachine Learning in HealthcareStatistical Methods in Clinical Trials

Targeted learning with daily EHR data

Open access
Sofrygin, Oleg, Zhu, Zheng, Schmittdiel, Julie A., Adams, Alyce S., Grant, Richard W., van der Laan, Mark J., Neugebauer, Romain·2019·Statistics in Medicine
DOI 10.1002/sim.8164
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Abstract

Electronic health records (EHR) data provide a cost‐ and time‐effective opportunity to conduct cohort studies of the effects of multiple time‐point interventions in the diverse patient population found in real‐world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow‐up into coarser intervals of pre‐specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated. We start filling these gaps by leveraging large‐scale EHR data from a diabetes study to develop a scalable targeted learning approach that allows analyses with small intervals. We then study the practical effects of selecting different coarsening intervals on inferences by reanalyzing data from the same large‐scale pool of patients. Specifically, we map daily EHR data into four analytic datasets using 90‐, 30‐, 15‐, and 5‐day intervals. We apply a semiparametric and doubly robust estimation approach, the longitudinal Targeted Minimum Loss‐Based Estimation (TMLE), to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the “long‐format TMLE,” and rely on the latest advances in scalable data‐adaptive machine‐learning software,xgboost and h2o, for estimation of the TMLE nuisance parameters.

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  • Inaccurate and uncertain outcomes are possible based on the collection and inspection of manually collected and stored data.

    “Inaccurate and uncertain outcomes are possible based on the collection and inspection of manually collected and stored data [72].”

    From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [72]

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Authors
Sofrygin, O., Zhu, Z., Schmittdiel, J. A., Adams, A. S., Grant, R. W., van der Laan, M. J., & Neugebauer, R.
Affiliations
  • Kaiser Permanente (US)
  • Division of Biostatistics University of California Berkeley California
Corresponding author
Sofrygin, Oleg
ORCID
Venue
Statistics in Medicine
Publisher
Wiley
Published
2019-04-25
Volume
38
Issue
16
Pages
3073-3090
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.8164
OpenAlex
W2620300882
Cited by
35
References
49
Citations by year
2026: 1 · 2025: 2 · 2024: 2 · 2023: 8 · 2022: 4 · 2021: 4 · 2020: 10 · 2019: 4
FWCI
4.30
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Statistics in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Patient-Centered Outcomes Research Institute (ME-1403-12506)
  • Institut National de la Santé et de la Recherche Médicale (R01 AI074345-07)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
targeted minimum loss-based estimation, causal effect estimation, diabetes mellitus, scalable machine learning, XGBoost, water, doubly robust estimation, semiparametric efficiency
MeSH terms
Machine Learning, Algorithms, Computer Simulation, Diabetes Mellitus, Humans, Longitudinal Studies, Reproducibility of Results, Causality, Electronic Health Records

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