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Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.

Matthew E Levine1, David J Albers1, George Hripcsak2

  • 1Department of Biomedical Informatics, Columbia University Medical Center, 622 W. 168th Street, Presbyterian Building 20th Floor, New York, NY 10032, United States; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, United States.

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Lagged linear regression effectively detects drug effects in electronic health records (EHR). Methodological choices significantly impact performance, highlighting the need for careful evaluation in time-series analysis of EHR data.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Health Data Science

Background:

  • Electronic Health Records (EHR) contain vast longitudinal patient data.
  • Detecting physiological drug effects from EHR data presents analytical challenges.
  • Lagged linear regression is a potential method for analyzing time-series EHR data.

Purpose of the Study:

  • To systematically evaluate the impact of methodological variations on lagged linear regression performance for detecting drug effects in EHR data.
  • To identify optimal pre-processing and modeling strategies for time-series analysis of EHR data.
  • To compare the performance of different methodological combinations against gold standards.

Main Methods:

  • Conducted a systematic examination of six methodological variations in lagged linear regression.
  • Generated two gold standards (knowledge-base derived and expert-curated) for drug-laboratory relationships.
  • Evaluated 64 unique combinations of methodological perturbations on 28 patient cohorts from a clinical database.

Main Results:

  • The most accurate methods achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.794 (knowledge-base) and 0.705 (expert-curated).
  • Specific methodological choices, including time re-parameterization and model type (autoregressive with differencing or independent lag without differencing), significantly influenced performance.
  • Suboptimal methodological choices resulted in performance near chance (AUROC ~0.5).

Conclusions:

  • Methodological choices in time-series analysis of EHR data have a substantial impact on detecting drug effects.
  • Beneficial pre-processing and modeling methodologies were identified, crucial for accurate analysis.
  • Continued careful evaluation of methodological perturbations is essential for advancing machine learning in EHR research.