Analysis of Competing Risks Data with Covariates Subject to Detection Limits
提出一种新的多重插补方法，用于处理协变量因检测限而删失时的竞争风险数据回归分析，基于Fine-Gray子分布风险模型，通过拒绝抽样迭代插补删失协变量，提升估计效率。
Competing risks data are commonly encountered in biomedical studies when subjects may experience multiple types of events and the occurrence of the primary event of interest can be precluded by a competing event. Challenges arise for regression analysis of such data with covariates subject to censoring due to detection limits. We propose a novel multiple imputation method for inference under Fine-Gray’s subdistribution hazard model with censored covariates subject to detection limits. Our proposal uses the information from the fully observed covariate values and the failure outcomes to impute the censored covariates iteratively using rejection sampling, which makes the imputation model compatible to the substantive model and the estimation efficiency improve significantly. We show the consistency and asymptotic normality of the resulting estimator and demonstrate its promising finite sample performance through simulation studies. Moreover, we extend this new proposal to assess the impacts of censored covariates on the predictive performance of the competing risks model. To illustrate its practical utility, we provide an application to the data from a study of community acquired pneumonia. Supplementary materials for this article are available online.