Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer
以主分层连接潜在类别生存模型：早期肾癌患者的异质性生存亚组AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用主分层评估不同治疗下的潜在生存亚组及异质性治疗效果。
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以主分层连接潜在类别生存模型：早期肾癌患者的异质性生存亚组
- 研究问题
- 如何识别早期肾癌患者在两种治疗下具有不同死亡风险的潜在亚组，并评估哪些患者可能从积极治疗中受益或受损？
- 方法与思路
- 作者以主分层框架连接两治疗组中的潜在生存类别，结合 Weibull 混合模型，估计高低死亡风险亚组的比例与特征。
- 材料陈述的贡献
- 摘要强调，主分层所依据的生存类别属于潜变量，并将方法用于 SEER-Medicare 数据，以研究治疗效果的异质性。
- 阅读关注点
- 核对两种治疗下潜在类别的联合识别、治疗分配与删失的处理，以及 Weibull 混合设定如何影响受益或受损亚组的判断。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，不能推断刊本变化。摘要未说明因果识别假设、潜在类别识别条件或实际治疗效果数值。
原文摘要
Rates of kidney cancer have been increasing, with small incidental tumors experiencing the fastest growth rates. Much of the increase could be due to increased use of CT scans, MRIs, and ultrasounds for unrelated conditions. Many tumors might never have been detected or become symptomatic in the past. This suggests that many patients might benefit from less aggressive therapy, such as active surveillance by which tumors are surgically removed only if they become sufficiently large. However, it has been difficult for clinicians to identify subgroups of patients for whom treatment might be especially beneficial or harmful. In this work, we use a principal stratification framework to estimate the proportion and characteristics of individuals who have large or small hazard rates of death in two treatment arms. This allows us to assess who might be helped or harmed by aggressive treatment. We also use Weibull mixture models. This work differs from much previous work in that the survival classes upon which principal stratification is based are latent variables. That is, survival class is not an observed variable. We apply this work using Surveillance Epidemiology and End Results-Medicare claims data. Clinicians can use our methods for investigating treatments with heterogeneous effects.