Dynamic treatment effect phenotyping through functional survival analysis
通过函数型生存分析刻画动态处理效应类型AI 解读 ↓
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通过函数型生存分析刻画动态处理效应类型
- 研究问题
- 对于事件发生时间结果，如何在处理效应随时间变化且个体反应不同的情况下，识别由效应修饰变量决定的动态反应模式？
- 方法与思路
- 作者提出受Royston–Parmar模型启发的样条生存神经网络，估计时变条件处理效应，再对效应曲线进行函数型聚类。
- 材料陈述的贡献
- 摘要称，方法不要求处理效应满足比例风险假设，并用模拟评估网络表现与聚类稳定性，以心衰患者停药数据展示应用。
- 阅读关注点
- 应核对处理效应使用何种尺度、电子健康记录中的混杂与删失如何处理，以及曲线估计不确定性是否传递至聚类结果。
- 阅读边界
- 这是arXiv预印本，同行评审或接收状态未核实；摘要未说明因果识别条件及删失假设，不能仅凭曲线分组判断停药的安全性。
原文摘要
In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often overlooked in clinical studies. Statistical tools are needed for the identification of treatment response patterns, taking into account that treatment response is not constant over time. We aim to provide an innovative method to obtain dynamic treatment effect phenotypes on a time-to-event outcome, conditioned on a set of relevant effect modifiers. The proposed method does not require the assumption of proportional hazards for the treatment effect, which is rarely realistic. We propose a spline-based survival neural network, inspired by the Royston-Parmar survival model, to estimate time-varying conditional treatment effects. We then exploit the functional nature of the resulting estimates to apply a functional clustering of the treatment effect curves in order to identify different patterns of treatment effects. The application that motivated this work is the discontinuation of treatment with Mineralocorticoid receptor Antagonists (MRAs) in patients with heart failure, where there is no clear evidence as to which patients it is the safest choice to discontinue treatment and, conversely, when it leads to a higher risk of adverse events. The data come from an electronic health record database. A simulation study was performed to assess the performance of the spline-based neural network and the stability of the treatment response phenotyping procedure. In light of the results, the suggested approach has the potential to support personalized medical choices by assessing unique treatment responses in various medical contexts over a period of time.