A Causal Inference Framework for Leveraging External Controls in Hybrid Trials

• URL: http://arxiv.org/abs/2305.08969v1
• Date: Mon, 15 May 2023 19:15:32 GMT
• Title: A Causal Inference Framework for Leveraging External Controls in Hybrid Trials
• Authors: Michael Valancius, Herb Pang, Jiawen Zhu, Stephen R Cole, Michele Jonsson Funk, Michael R Kosorok
• Abstract summary: We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source. We propose estimators, review efficiency bounds, and an approach for efficient doubly-robust estimation. We apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy.
• Score: 1.7942265700058988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish the connection to a novel graphical criteria. We propose estimators, review efficiency bounds, develop an approach for efficient doubly-robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, and demonstrate finite-sample performance through a simulation study. To illustrate the ideas and methods, we apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy for which there exists an external set of control patients from a previous trial.

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