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A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort
Sajjad Karimi1, Robert Kluger1
1Department of Civil and Environmental Engineering, University of Louisville, W.S. Speed, Room 112, Louisville, KY 40292, United States.
Accident; Analysis and Prevention
|January 11, 2026
Summary
Estimating injury severity from linked data is challenging due to bias. Augmented Inverse Probability Weighting (AIPW) offers a robust solution for accurate trauma research and resource allocation.
Area of Science:
- Trauma research
- Epidemiology
- Biostatistics
Background:
- Accurate injury severity estimation is crucial for trauma care, crash intervention evaluation, and EMS resource allocation.
- Linked administrative datasets are often compromised by incomplete linkage and selection bias, affecting analysis reliability.
Purpose of the Study:
- To address potential bias in injury severity estimation when integrating multiple datasets.
- To compare a doubly robust estimation framework (AIPW) against traditional methods for injury severity analysis.
Main Methods:
- Employed a doubly robust estimation framework using Augmented Inverse Probability Weighting (AIPW).
- Utilized multi-source linked data from crash, EMS, and trauma records.
- Estimated Injury Severity Score (ISS) using naïve complete-case analysis, inverse probability weighting (IPW), and AIPW.
Main Results:
- Naïve analysis yielded a mean ISS of 13.52; IPW (10.86) and AIPW (10.93) provided adjusted estimates.
- AIPW revealed substantial differences in risk and protective factor associations compared to naïve analysis (e.g., male gender impact on ISS).
- Traditional analyses may underestimate or misstate key associations, while AIPW offers more accurate population-level inferences.
Conclusions:
- The proposed AIPW framework provides a statistically rigorous and practical solution for injury severity research using linked administrative data.
- Adjusting for selection bias is essential for accurate estimation of injury severity and associated factors.
- This method improves the reliability of findings for trauma care, intervention evaluation, and resource allocation.
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