Interpreting Net Survival: What We Estimate Versus What We Think We Estimate
如何解释净生存率：实际估计目标与通常理解的差异AI 解读 ↓
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如何解释净生存率：实际估计目标与通常理解的差异
- 研究问题
- 癌症患者的其他死因风险高于一般人群时，净生存估计是否仍对应仅存在癌症死因的反事实生存概率？
- 方法与思路
- 作者将总死亡分解为癌症、基线健康差异、治疗诱发及一般人群其他死因，分析 Pohar Perme 估计的实际目标。
- 材料陈述的贡献
- 摘要称，该估计仍包含基线差异和治疗诱发的额外死亡，建议解释为去除一般人群其他死因后的生存率。
- 阅读关注点
- 应核对死亡成分分解的定义、患者与一般人群风险可比性，以及治疗诱发死亡为何不能通过更细分层消除。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未提供完整分解条件和计算细节，所述经验风险差异需核对原始证据。
原文摘要
Net survival is conventionally defined as ``survival if cancer were the only possible cause of death'', an estimand corresponding to cancer-specific mortality alone. The Pohar Perme estimator targets this by removing general population other-cause mortality from observed total mortality, but achieves it only when cancer patients experience the same other-cause mortality as the general population. However, cancer patients often experience elevated other-cause mortality due to baseline health differences and treatment-induced effects. Using recent theoretical work decomposing total mortality into four components (cancer deaths, baseline health differences, treatment-induced other-cause deaths, and general population other-cause mortality), we show that the Pohar Perme estimator delivers the sum of cancer deaths, baseline differences, and treatment-induced deaths, falling short of its intended estimand whenever either source of excess is present. From Botta \textit{et al}, we present empirical evidence showing relative risk of other-cause deaths ranging from 1.0 (colorectal cancer) to 4.0+ (head and neck cancers), and calculations demonstrating that net survival can substantially underestimate cancer-specific survival probability when relative risk exceeds 1.0. Critically, treatment-induced other-cause deaths represent irreducible causal pathways from cancer to death that cannot be eliminated through better stratification. We recommend interpreting net survival as ``survival where general population other-cause mortality is removed'' rather than as a causal counterfactual, and call for more precise language in cancer epidemiology.