# Treatment effect estimation using the propensity score in clinical trials with historical control

## TL;DR

This paper introduces a new statistical method using propensity scores to better estimate treatment effects in clinical trials that use historical control data.

## Contribution

The novel contribution is incorporating information on whether data are from RCT or historical control into the propensity score model.

## Key findings

- The proposed method performs similarly to conventional methods when covariate distributions are similar between RCT and historical data.
- When covariate distributions differ, the proposed method outperforms conventional approaches in estimating treatment effects.
- The new method is useful even when similarity between RCT and historical data is unknown.

## Abstract

Clinical trials assessing new treatment effects require a control group to compare the pure treatment effects. However, in clinical trials on regenerative medicine, rare diseases, and intractable diseases, it may be ethically difficult to assign participants to the control group. In recent years, the use of historical control data has attracted attention as a method for supplementing the number of participants in the control group. When combining historical control data with new randomized controlled trial (RCT) data, the assessment of heterogeneity using outcome data is not sufficient. Therefore, several statistical methods that consider participant outcomes and baseline characteristics, including the propensity score (PS) method have been proposed.

We propose a new method considering “information on whether the data are RCT data or not” in the PS model when combining the RCT and historical control data. The performance of the proposed method in estimating the treatment effect is evaluated using simulation data.

When the distribution of covariates is similar between the RCT and historical control data, not much difference in performance is found between the proposed and conventional methods to estimate the treatment effect. On the other hand, when the distribution of covariates is not similar between the two kinds of data, the proposed method shows higher performance.

Even when it is not known whether RCT and historical control data are similar, the proposed PS model is useful to estimate the treatment effect appropriately in RCTs using historical control data.

The online version contains supplementary material available at 10.1186/s12874-023-02127-9.