Framework for Research in Equitable Synthetic Control Arms

Area of Science:

• Clinical Trials Methodology
• Health Equity Research
• Biostatistics and Epidemiology

Background:

• Randomized Clinical Trials (RCTs) are vital for efficacy but often lack generalizability due to equity concerns.
• Representativeness in RCTs is a growing national priority.
• Synthetic Controls (SCs) offer efficiency but rarely consider equity in augmenting RCTs.

Purpose of the Study:

• To investigate methods for improving treatment effect estimation and equity in clinical trials.
• To introduce a Hybrid Control Arm (HCA) by augmenting concurrent controls with SCs.
• To develop a framework (FRESCA) for evaluating HCA construction methods.

Main Methods:

• Utilized RCT simulations within the FRESCA framework.
• Examined the impact of propensity and equity adjustments in HCA construction.
• Assessed treatment effect estimation accuracy and equity goal achievement.

Main Results:

• Propensity and equity adjustments during HCA construction yield accurate population treatment effect estimates.
• Hybrid Control Arms can meet equity goals while potentially reducing the number of on-trial patients.
• FRESCA provides a robust method for evaluating HCA designs.

Conclusions:

• This study pioneers the investigation of equity in Hybrid Control Arm design.
• The findings suggest HCAs can enhance both the accuracy and equity of clinical trials.
• The work offers definitions, metrics, and resources for future research on equitable trial design.