Proximal survival analysis to handle dependent right censoring
处理依赖性右删失的近端生存分析AI 解读 ↓
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处理依赖性右删失的近端生存分析
- 研究问题
- 当观测协变量不能消除事件时间与右删失之间的依赖时，如何把协变量作为潜在关联的代理，识别并估计生存目标？
- 方法与思路
- 作者建立允许条件独立删失失效的非参数识别框架，以不完美代理变量刻画潜在关联，并据此提出自适应估计量。
- 材料陈述的贡献
- 摘要称在相应一般条件下，估计量具有一致性、渐近正态性和双重稳健性，并用蒙特卡洛及SEER-Medicare数据考察。
- 阅读关注点
- 应核对代理变量与潜在关联的条件、实际识别的生存目标，以及双重稳健性的模型组合和渐近推断所需条件。
- 阅读边界
- 仅据所给Crossref摘要；摘要未说明代理识别的具体假设、模拟数值或应用结论，不能认为任意协变量都足以解决依赖性删失。
原文摘要
Abstract Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assuming censoring and failure time are independent within covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with absolute certainty. In cases of dependent censoring, covariate measurements are often, at best, proxies of underlying prognoses. In this article, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators, and we provide generic assumptions under which they are consistent, asymptotically normal, and doubly robust. We examine the finite-sample performance of our proposed estimators via a Monte Carlo simulation and apply them to the SEER-Medicare dataset.