Modeling the restricted mean survival time using pseudo-value random forests
利用伪值随机森林建模限制平均生存时间AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确包含混杂调整后的处理对比及 RMST 因果分析，因此归入因果推断。
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利用伪值随机森林建模限制平均生存时间
- 研究问题
- 如何在不要求比例风险结构的情况下，灵活估计给定基线变量的个体限制平均生存时间，并构造混杂调整后的处理对比？
- 方法与思路
- 作者将留一法 jackknife 伪值作为随机森林回归的响应，直接建模条件 RMST，并用模拟及乳腺癌试验数据展示应用。
- 材料陈述的贡献
- 摘要称，PVRF 可灵活识别高维协变量效应，无需比例风险假设，数值实验显示个体 RMST 与基于 RMST 的处理对比估计准确。
- 阅读关注点
- 应核对删失机制与伪值构造条件、时间上限的选择，以及条件预测如何转为处理对比和因果识别所需的额外条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收均未核实；无需比例风险不代表无需删失或因果假设，摘要未给出这些条件。
原文摘要
The restricted mean survival time (RMST) has become a popular measure to summarize event times in longitudinal studies. Defined as the area under the survival function up to a time horizon $τ$ > 0, the RMST can be interpreted as the life expectancy within the time interval [0, $τ$]. In addition to its straightforward interpretation, the RMST also allows for the definition of valid estimands for the causal analysis of treatment contrasts in medical studies. In this work, we introduce a non-parametric approach to model the RMST conditional on a set of baseline variables (including, e.g., treatment variables and confounders). Our method is based on a direct modeling strategy for the RMST, using leave-one-out jackknife pseudo-values within a random forest regression framework. In this way, it can be employed to obtain precise estimates of both patient-specific RMST values and confounder-adjusted treatment contrasts. Since our method (termed "pseudo-value random forest", PVRF) is model-free, RMST estimates are not affected by restrictive assumptions like the proportional hazards assumption. Particularly, PVRF offers a high flexibility in detecting relevant covariate effects from higher-dimensional data, thereby expanding the range of existing pseudo-value modeling techniques for RMST estimation. We investigate the properties of our method using simulations and illustrate its use by an application to data from the SUCCESS-A breast cancer trial. Our numerical experiments demonstrate that PVRF yields accurate estimates of both patient-specific RMST values and RMST-based treatment contrasts.