  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.
