Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies

• VanderWeele, Tyler J. ^[1] ; Mathur, Maya B. ^[1] ; Chen, Ying ^[1]
  1. [1] Harvard University

     Harvard University

     City of Cambridge, Estados Unidos

     
• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 3, 2020 (Ejemplar dedicado a: Causal Inference), págs. 437-466
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• 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.