审查(临床试验)
协变量
统计
比例危险模型
估计员
逆概率加权
反概率
计量经济学
生存分析
回归分析
数学
回归
贝叶斯概率
后验概率
作者
Rong Rong,Jing Ning,Hong Zhu
摘要
Abstract The restricted mean survival time (RMST) is a clinically meaningful summary measure in studies with survival outcomes. Statistical methods have been developed for regression analysis of RMST to investigate impacts of covariates on RMST, which is a useful alternative to the Cox regression analysis. However, existing methods for regression modeling of RMST are not applicable to left‐truncated right‐censored data that arise frequently in prevalent cohort studies, for which the sampling bias due to left truncation and informative censoring induced by the prevalent sampling scheme must be properly addressed. The pseudo‐observation (PO) approach has been used in regression modeling of RMST for right‐censored data and competing‐risks data. For left‐truncated right‐censored data, we propose to directly model RMST as a function of baseline covariates based on POs under general censoring mechanisms. We adjust for the potential covariate‐dependent censoring or dependent censoring by the inverse probability of censoring weighting method. We establish large sample properties of the proposed estimators and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed methods to a prevalent cohort of women diagnosed with stage IV breast cancer identified from surveillance, epidemiology, and end results‐medicare linked database.
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