2023/02/05 by Nan Qiao, Wangcheng Li, Qiao, Nan +6
Mathematics · Medicine · #Computation (stat.CO) #FOS: Computer and information sciences #Liver Disease Diagnosis and Treatment #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2302.02286
openalex publication_date 2023/02/05 · openalex created_date 2023/02/09 · openalex updated_date 2026/07/28
The use of massive survival data has become common in survival analysis. In this study, a subsampling algorithm is proposed for the Cox proportional hazards model with time-dependent covariates when the sample is extraordinarily large but computing resources are relatively limited. A subsample estimator is developed by maximizing the weighted partial likelihood; it is shown to have consistency and asymptotic normality. By minimizing the asymptotic mean squared error of the subsample estimator, the optimal subsampling probabilities are formulated with explicit expressions. Simulation studies show that the proposed method can satisfactorily approximate the estimator of the full dataset. The proposed method is then applied to corporate loan and breast cancer datasets, with different censoring rates, and the outcomes confirm its practical advantages.