Statistical grand rounds: design and analysis of studies with binary- event composite endpoints: guidelines for anesthesia research.

Composite endpoints consisting of several binary events, such as distinct perioperative complications, are frequently chosen as the primary outcome in anesthesia studies (and in many other clinical specialties) because (1) no single outcome fully characterizes the disease or outcome of interest, and/or (2) individual outcomes are rare and statistical power would be inadequate for any single one. Interpreting a composite endpoint is challenging because components rarely meet the ideal criteria of having comparable clinical importance, frequency, and treatment effects. We suggest guidelines for forming composite endpoints and show advantages of newer versus conventional statistical methods for analyzing them. Components should be a parsimonious set of outcomes, which when taken together, well represent the disease of interest and are very plausibly related to the intervention. Adding components that are too narrow, redundant, or minimally influenced by the study intervention compromises interpretation of results and reduces power. We show that multivariate (i.e., multiple outcomes per patient) methods of analyzing a binary-event composite provide distinct advantages over standard methods such as any-versus-none, count of events, or evaluation of individual events. Multivariate methods can incorporate clinical importance weights, compensate for events occurring at varying frequencies, assess treatment effect heterogeneity, and are often more powerful than alternative statistical approaches. Methods are illustrated with an American College of Surgeons National Surgical Quality Improvement Program registry study that evaluated the effects of smoking on major perioperative outcomes, and with a clinical trial comparing the effects of crystalloids and colloids on major complications. Sample data files and SAS code are included for convenience.