Multiple Imputation Of Competing Risks Data With Missing Cause Of Failure In Additive Hazards Model

Competing risks data arise when study subjects may experience several different types of failure. It is common that the cause of failure is missing due to various reasons. Analysis of competing risks data with missing cause of failure has received considerable attention recently. In this article, we study the semiparametric additive hazards model for analysis of competing risk data with missing cause of failure. We apply the multiple imputation (MI) method and evaluate its comparative performance under various missing data scenarios. Results from simulation experiments showed that, compared to naive techniques, the MI-based method gave estimates with much smaller biases and standard of errors closer to the nominal level. And all methods were also applied on the real data of Bone Marrow Transplant...