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Related Experiment Video

Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Handing missing data using multiple imputation in hybrid control clinical trials with modified power prior.

Sunao Shimada¹, Masataka Taguri¹

¹Department of Health Data Science, Tokyo Medical University, Tokyo, Japan.

Journal of Biopharmaceutical Statistics

|June 11, 2026

Summary

This study introduces a method for handling missing data in historical control data for hybrid clinical trials. Multiple imputation with propensity scores and power priors effectively reduces bias and improves precision, especially when historical data resembles current trial data.

Keywords:

hybrid control missing covariate data modified power prior multiple imputation propensity score matching

Related Experiment Videos

Last Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Biostatistics
Clinical Trial Design
Real-World Data Analysis

Background:

Randomized Controlled Trials (RCTs) are the benchmark for clinical evidence.
Hybrid control trials incorporating Historical Data (HD) can enhance evidence generation.
Real-world data (RWD) used for HD often contains missing covariate information, potentially introducing bias.

Purpose of the Study:

To propose and evaluate a statistical method for addressing missing covariate data in HD for hybrid control trials.
To assess the performance of multiple imputation combined with propensity score matching and power prior methods.
To investigate bias and precision under different missing data mechanisms and data similarity scenarios.

Main Methods:

Employed multiple imputation under the Missing At Random (MAR) assumption to handle missing covariates.
Utilized propensity score matching and a modified power prior approach for data analysis.
Conducted simulations to compare proposed methods against complete case analysis and applied the method to real clinical trial data.

Main Results:

Complete case analysis exhibited bias with missing at random and covariate-dependent missingness.
Multiple imputation yielded nearly unbiased estimates and improved precision when HD was similar to current trial data (MAR assumption).
The method dynamically borrowed information from HD based on outcome similarity, enhancing accuracy and power while controlling Type I error.

Conclusions:

The proposed method effectively handles missing covariate data in HD for hybrid control trials.
Multiple imputation offers a robust solution, outperforming complete case analysis.
The approach demonstrates practical utility and potential for improving hybrid trial designs using RWD.