title: Proportional hazards model for competing risks data with missing cause of failure.
abstract: We consider the semiparametric proportional hazards model for the cause-specific hazard function in analysis of competing risks data with missing cause of failure. The inverse probability weighted equation and augmented inverse probability weighted equation are proposed for estimating the regression parameters in the model, and their theoretical properties are established for inference. Simulation studies demonstrate that the augmented inverse probability weighted estimator is doubly robust and the proposed method is appropriate for practical use. The simulations also compare the proposed estimators with the multiple imputation estimator of Lu and Tsiatis (2001). The application of the proposed method is illustrated using data from a bone marrow transplant study.
authors.affiliation: Division of Mathematics and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA.
authors.forename: Seunggeun
authors.initials: S
authors.lastname: Hyun
authors.forename: Jimin
authors.initials: J
authors.lastname: Lee
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Sun