Multi-Study Causal Forest (MCF): A flexible framework for data borrowing in the presence of varying treatment effect heterogeneity

TL;DR

The paper introduces the Multi-Study Causal Forest (MCF), a flexible framework that leverages auxiliary data sources to improve individualized treatment effect estimation amid heterogeneity across and within studies.

Contribution

It presents the MCF framework, enabling data borrowing from auxiliary sources while accounting for heterogeneity, advancing personalized treatment effect estimation methods.

Findings

MCF outperforms existing methods in simulations with varying heterogeneity.

MCF effectively combines data from RCTs and observational studies.

Application to breast cancer data shows improved treatment effect estimates.

Abstract

Tailoring treatment assignment to specific individuals can improve the health outcomes, but a single study may offer inadequate information for this purpose. The ability to leverage information from an auxiliary data source deemed to be `most similar' to a primary data source has been shown to improve estimates of treatment effects. In this paper, we introduce a framework, the Multi-Study Causal Forest (MCF), to borrow individual patient-level data from an auxiliary data source in the presence of `varying sources' of treatment effect heterogeneity. We utilise a simulation study to demonstrate the superiority of the MCF in the presence of varying treatment allocation models (between-study heterogeneity) in addition to being able to account for the presence of within-study heterogeneity. This approach can combine data from randomised controlled trials, observational studies or a combination of both. We illustrate using Breast cancer data that the MCF performs favourably compared to an existing methodology in the presence of varying sources of (both between and within) heterogeneity.