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

Creative Commons License

VanderWeele T. J., Mathur M. B., Chen Y.

STATISTICAL SCIENCE, vol.35, no.3, pp.437-466, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.1214/19-sts728
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, zbMATH, DIALNET
  • Page Numbers: pp.437-466
  • Keywords: Causal inference, confounding, multiple testing, sensitivity analysis, bias, longitudinal data, SENSITIVITY-ANALYSIS, INSTRUMENTAL VARIABLES, PROPENSITY SCORE, BIAS, REGRESSION, INTERVENTIONS, EPIDEMIOLOGY, METAANALYSIS, EXPOSURE, ATTENUATION
  • Middle East Technical University Affiliated: No


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.