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Treatment effect estimation using the propensity score in clinical trials with historical control
Saki Kanamori1, Masahiro Takeuchi2,3
1Department of Clinical Medicine (Biostatistics), Graduate School of Pharmaceutical Sciences, Kitasato University, 5-9-1, Shirokane, Minato-ku, Tokyo, 108-8641, Japan. kanamoris@pharm.kitasato-u.ac.jp.
BMC Medical Research Methodology
|February 23, 2024
Summary
A new propensity score (PS) model improves treatment effect estimation when combining randomized controlled trial (RCT) data with historical controls. This method is particularly effective when covariate distributions differ between datasets.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Health Economics
Background:
- Clinical trials necessitate control groups for treatment effect evaluation.
- Ethical considerations in rare or intractable disease trials limit control group assignment.
- Historical control data offers a solution to supplement control groups in clinical trials.
Purpose of the Study:
- To propose a novel propensity score (PS) model for integrating randomized controlled trial (RCT) and historical control data.
- To evaluate the performance of the proposed method in estimating treatment effects.
Main Methods:
- Development of a new PS model incorporating data source (RCT vs. historical).
- Evaluation of the proposed method using simulation data.
- Comparison with conventional methods for treatment effect estimation.
Main Results:
- Similar performance between proposed and conventional methods when covariate distributions align.
- Superior performance of the proposed method when covariate distributions differ significantly between RCT and historical data.
Conclusions:
- The proposed PS model effectively estimates treatment effects in RCTs utilizing historical control data.
- This method is valuable even when the similarity of covariate distributions is unknown.
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