title: Another view on the analysis of cardiovascular morbidity/mortality trials.
abstract: In many morbidity/mortality studies, composite endpoints are considered. Although the primary interest is to demonstrate that an invention delays death, the expected death rate is often that low that studies focussing on survival exclusively are not feasible. Components of the composite endpoint are chosen such that their occurrence is predictive for time to death. Therefore, if the time to non-fatal events is censored by death, censoring is no longer independent. As a consequence, the analysis of the components of a composite endpoint cannot be reasonably performed using classical methods for the analysis of survival times like Kaplan-Meier estimates or log-rank tests. In this paper we visualize the impact of disregarding dependent censoring during the analysis and discuss practicable alternatives for the analysis of morbidity/mortality studies. In the context of simulations we provide evidence that copula-based methods have the potential to deliver practically unbiased estimates of hazards of components of a composite endpoint. At the same time, they require minimal assumptions, which is important since not all assumptions are generally verifiable because of censoring. Therefore, there are alternative ways to analyze morbidity/mortality studies more appropriately by accounting for the dependencies among the components of composite endpoints. Despite the limitations mentioned, these alternatives can at minimum serve as sensitivity analyses to check the robustness of the currently used methods.
authors.affiliation: Novartis Pharma AG, Basel, Switzerland. gerd.rosenkranz@novartis.com
authors.forename: Gerd K
authors.initials: GK
authors.lastname: Rosenkranz
mesh_terms: D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000401:mortality; D002986:Clinical Trials as Topic; D006801:Humans; D053208:Kaplan-Meier Estimate; D016013:Likelihood Functions; D015233:Models, Statistical; D008962:Models, Theoretical; D009026:Mortality; D011336:Probability; D012107:Research Design / Q000706:statistics & numerical data*; D013223:Statistics as Topic