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Updated: Jul 2, 2026
06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Methods for incorporating test result information within the high-dimensional propensity score framework: application
John Tazare1, Jeremy P Brown2,3, Daniel R Morales4
1Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK. john.tazare1@lshtm.ac.uk.
BMC Medical Research Methodology
|July 1, 2026
Summary
Integrating laboratory test results into high-dimensional propensity scores (HDPS) can reduce confounding in electronic health record studies. This approach, using UK data, showed improved estimates for proton pump inhibitor use and COPD mortality.
Area of Science:
- Health Informatics
- Epidemiology
- Biostatistics
Background:
- Electronic health records (EHRs) offer vast laboratory test data for confounding control.
- Optimal integration of test results into high-dimensional propensity score (HDPS) models is unclear.
Purpose of the Study:
- To develop and evaluate methods for incorporating laboratory test data into HDPS.
- To assess the impact of test data on confounding in real-world EHR studies.
Main Methods:
- Proposed novel methods to integrate laboratory test data (cut-offs, missing indicators) into HDPS.
- Utilized UK Clinical Practice Research Datalink GOLD data for a comparative study.
- Estimated hazard ratios (HRs) using Cox models weighted by inverse HDPS.
Main Results:
- HDPS models incorporating test data reduced confounding, attenuating HR for PPI use and COPD mortality (1.36 to 1.24).
- 46% of HDPS covariates were derived from test-related data, highlighting their importance.
- Methods addressed data quality and missing data concerns for test results.
Conclusions:
- Empirically demonstrated that laboratory test results can significantly reduce residual confounding in UK EHR studies.
- Proposed principled approaches for integrating laboratory test data into data-driven confounding control.
- Advocated for the inclusion of test information within HDPS when available.
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