Invited Commentary: Two-Phase, Generalized Case-Control Designs for Quantitative Longitudinal Outcomes and Evolution of the Case-Control Study.

The case-control study design has evolved substantially over the past half century. The design has long been recognized as a way to increase efficiency by studying fewer subjects than would be required for a full cohort study. Historically, it was thought that case-control studies required a rare disease assumption for valid risk ratio estimation, but it was later realized that rare disease was not necessary. Over time, the design and analysis methods were further modified to allow estimation of rate ratios or to allow each person to serve as his/her own control (as we see with case-cohort and case-crossover studies, for example). We now understand that efficiency can be increased through the use of outcome-dependent sampling not only for dichotomous outcomes but also for continuous outcomes in longitudinal studies with repeated outcome measurement during follow-up. In their accompanying paper, Schildcrout et al. (Am J Epidemiol. 2019;000(00):000-000) contribute to our understanding, clearly summarizing many recent advances in study design and analyses that allow more general and efficient use of case-control studies. Their simulations demonstrate that improved efficiency is achieved with these methods when the goal is to estimate associations of exposure with trajectories and patterns of change over time. Here we comment on application of some of these generalized case-control methods to causal inference.