Data fusion for efficiency gain in ATE estimation: A practical review with simulations

TL;DR

This paper reviews data fusion methods combining RWD and RCTs to improve causal effect estimation, highlighting their assumptions, limitations, and trade-offs through simulations, aiding researchers in method selection.

Contribution

It provides a systematic comparison of data fusion methods for causal inference, including simulation-based insights into their trade-offs and practical considerations.

Findings

Identifies a prevalent risk-reward trade-off among methods

Provides insights into assumptions and limitations of each method

Helps researchers select appropriate data fusion techniques

Abstract

The integration of real-world data (RWD) and randomized controlled trials (RCT) is increasingly important for advancing causal inference in scientific research. This combination holds great promise for enhancing the efficiency of causal effect estimation, offering benefits such as reduced trial participant numbers and expedited drug access for patients. Despite the availability of numerous data fusion methods, selecting the most appropriate one for a specific research question remains challenging. This paper systematically reviews and compares these methods regarding their assumptions, limitations, and implementation complexities. Through simulations reflecting real-world scenarios, we identify a prevalent risk-reward trade-off across different methods. We investigate and interpret this trade-off, providing key insights into the strengths and weaknesses of various methods; thereby helping researchers navigate through the application of data fusion for improved causal inference.