Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies
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VanderWeele, Tyler J. [1] ; Mathur, Maya B. [1] ; Chen, Ying [1]
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[1] Harvard University
Harvard University
City of Cambridge, Estados Unidos
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- Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 3, 2020 (Ejemplar dedicado a: Causal Inference), págs. 437-466
- Idioma: inglés
- DOI: 10.1214/19-sts728
- Texto completo no disponible (Saber más ...)
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Resumen
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.
