Statisticians have contributed enormously to the conceptualization, development, and success of case-control methods for the study of disease causation and prevention. This article reviews the major developments. It starts with Cornfield's demonstration of odds ratio invariance under cohort versus case-control sampling, proceeds through the still-popular Mantel—Haenszel procedure and its extensions for dependent data, and highlights (conditional) likelihood methods for relative risk regression. Recent work on nested case-control, case-cohort, and two-stage case-control designs demonstrates the continuing impact of statistical thinking on epidemiology. The influence of R. A. Fisher's work on these developments is mentioned wherever possible. His objections to the drawing of causal conclusions from observational data on cigarette smoking and lung cancer are used to introduce the problems of measurement error and confounding bias. The resolution of such difficulties, whether by further development and implementation of randomized intervention trials or by causal analysis of observational data using graphical models containing latent variables, will challenge future generations of statisticians.