Analysis of a binary composite endpoint with missing data in components

Abstract

Composite endpoints are often used in clinical trials in order to increase the overall event rates, reduce the sizes of the trials and achieve desired power. For example, in a trial to study the effect of a treatment on the prevention of venous thromboembolic events after a major orthopaedic surgery of the lower limbs, the primary endpoint is usually a composite endpoint consisting of any deep vein thrombosis identified by systematic venography of lower limbs, symptomatic and well-documented non-fatal pulmonary embolism, and death from all causes. Just as any endpoints, missing data can occur in the components of the composite endpoint. If a patient has missing data on some of the components but not all the components, this patient may not have complete data but partial data for the composite endpoint. To be consistent with the intention-to-treat principle, the patient should not be discarded from the analysis. In this research, we propose an approach for the analysis of a composite endpoint with missing data in components. The main idea is to first derive the probabilities of all possible study outcomes based on the appropriate model and then to construct the overall rate for the composite endpoint. Simulations are conducted to compare the approach with several naïve methods. A data example is used to illustrate the application of the approach.