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Updated: Jun 12, 2026
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
A doubly robust estimator for unanchored indirect treatment comparisons: Development and evaluation using simulated
Yunhong Wu1, Yu Yin2, Yun Ling2
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Journal of Biopharmaceutical Statistics
|June 11, 2026
Summary
This study evaluates methods for indirect treatment comparisons (ITCs) when individual patient data (IPD) is unavailable. A novel doubly robust (DR) estimator performed well, and is recommended for such settings.
Area of Science:
- Health Economics
- Biostatistics
- Epidemiology
Background:
- Unanchored indirect treatment comparisons (ITCs) are increasingly used when head-to-head trials are absent.
- Estimating treatment effects across different data sources presents methodological challenges.
Purpose of the Study:
- To evaluate and compare methods for estimating treatment effects using two data sources for continuous and binary outcomes.
- To introduce a novel doubly robust (DR) estimator for settings lacking individual patient data (IPD).
Main Methods:
- The study employs the Neyman-Rubin causal framework to target the population average treatment effect for the treated.
- Six existing methods based on propensity score weighting and outcome regression were reviewed.
- A new doubly robust (DR) estimator was developed for scenarios where IPD is unavailable.
Main Results:
- Doubly robust (DR) estimators demonstrated consistent performance across various practical settings.
- The proposed DR estimator showed particular promise for situations with unavailable IPD.
Conclusions:
- The study recommends the proposed DR estimator for indirect treatment comparisons (ITCs) when individual patient data (IPD) is unavailable.
- Doubly robust methods offer a reliable approach for estimating treatment effects in data-scarce comparative effectiveness research.
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