Open Access
August 2020 Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies
Tyler J. VanderWeele, Maya B. Mathur, Ying Chen
Statist. Sci. 35(3): 437-466 (August 2020). DOI: 10.1214/19-STS728


In this paper, we propose a new template for empirical studies intended to assess causal effects: the outcome-wide longitudinal design. The approach is an extension of what is often done to assess the causal effects of a treatment or exposure using confounding control, but now, over numerous outcomes. We discuss the temporal and confounding control principles for such outcome-wide studies, metrics to evaluate robustness or sensitivity to potential unmeasured confounding for each outcome and approaches to handle multiple testing. We argue that the outcome-wide longitudinal design has numerous advantages over more traditional studies of single exposure-outcome relationships including results that are less subject to investigator bias, greater potential to report null effects, greater capacity to compare effect sizes, a tremendous gain in the efficiency for the research community, a greater policy relevance and a more rapid advancement of knowledge. We discuss both the practical and theoretical justification for the outcome-wide longitudinal design and also the pragmatic details of its implementation, providing publicly available R code.


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Tyler J. VanderWeele. Maya B. Mathur. Ying Chen. "Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies." Statist. Sci. 35 (3) 437 - 466, August 2020.


Published: August 2020
First available in Project Euclid: 11 September 2020

MathSciNet: MR4148220
Digital Object Identifier: 10.1214/19-STS728

Keywords: bias , Causal inference , confounding , longitudinal data , multiple testing , sensitivity analysis

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.35 • No. 3 • August 2020
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