Analysis of the cause-specific proportional hazards model with missing covariates

In the analysis of competing risks data, some of covariates may not be fully observed for some subjects.In such cases, excluding subjects with missing covariate values from the analysis may result in biased estimates and loss of efficiency.In this paper, we studied multiple imputation and the augmented inverse probability weighting method for regression parameter estimation in the cause-specific proportional hazards model with missing covariates.The performance of estimators obtained from multiple imputation and the augmented inverse probability weighting method is evaluated by simulation studies, which show that those methods perform well.Multiple imputation and the augmented inverse probability weighting method were applied to investigate significant risk factors for the risk of death from breast cancer and from other causes for breast cancer data with missing values for tumor size obtained from the Prostate, Lung, Colorectal, and Ovarian Cancer Screen Trial Study.Under the cause-specific proportional hazards model, the methods show that race, marital status, stage, grade, and tumor size are significant risk factors for breast cancer mortality, and stage has the greatest effect on increasing the risk of breast cancer death.Age at diagnosis and tumor size have significant effects on increasing the risk of other-cause death.