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Updated: Dec 18, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Unknown confounders did not bias the treatment effect when improving balance of known confounders in randomized
Oliver Kuss1, Matthaeus Miller2
1German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Institute for Biometrics and Epidemiology, Düsseldorf, Germany; Medical Faculty, Heinrich Heine University Düsseldorf, Institute of Medical Statistics, Düsseldorf, Germany.
Objective:
The objective of the study was to measure if improving balance in known and observed confounders by propensity score (PS) matching yields different treatment effect estimates in randomized controlled trials (RCTs), thus indirectly measuring the influence of unknown confounders.
Study Design And Setting:
This is an analysis of individual patient data of 26 large RCTs and comparison of agreement between PS-matched samples and the RCT results on one hand with the agreement between subsamples of RCTs (with sample sizes equal to the sample sizes of the PS-matched samples) and RCTs by Bland-Altman plots and corresponding intraclass correlation coefficients on the other.
Results:
We included data on 213 outcomes from 37 treatment comparisons with 193,620 patients from 26 trials. Bland-Altman plots and intraclass correlation coefficients showed better agreement between PS-matched analysis and RCTs than between reduced RCTs and RCTs.
Conclusion:
We found no indication for a detrimental influence of unknown confounders in PS-matched samples of RCTs.
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