Outcome-wide longitudinal designs for causal inference: a new template for empirical studies

Tyler J. VanderWeele; Maya B. Mathur; Ying Chen

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arXiv · 1810.10164

Outcome-wide longitudinal designs for causal inference: a new template for empirical studies

Tyler J. VanderWeele·Maya B. Mathur·Ying Chen

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Abstract

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.

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Tyler J. VanderWeele, Maya B. Mathur, Ying Chen. 2018-10-24. Outcome-wide longitudinal designs for causal inference: a new template for empirical studies. https://arxiv.org/abs/1810.10164

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