A class of nonparametric methods for evaluating the effect of continuous treatments on survival outcomes
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AI 根据所提供的论文材料归类：摘要明确以连续干预下的反事实生存概率为目标，研究处理因果效应的非参数检验。
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评估连续处理对生存结局影响的一类非参数方法
- 研究问题
- 在生存结局因右删失而仅部分可观测、处理变量连续的情况下，如何检验给定时间点的反事实生存概率是否随暴露水平变化？
- 方法与思路
- 作者针对连续暴露目标缺乏路径可微性的难点，发展非参数推断方法，检验反事实生存概率在整个连续暴露范围内是否恒定。
- 材料陈述的贡献
- 摘要称将推断从多水平干预扩展至连续暴露，提出无效应检验，并通过数值研究及两项 HIV 单克隆抗体有效性试验数据评价方法。
- 阅读关注点
- 应核对处理与删失的识别条件、恒定性原假设及检验统计量，并区分整体无效应检验与逐暴露水平生存概率估计的保证。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明具体识别条件、检验构造、功效结果或试验中的效应大小。
原文摘要
In randomized trials and observational studies, it is often necessary to evaluate the extent to which an intervention affects a time-to-event outcome, which is only partially observed due to right censoring. For instance, in infectious disease studies, it is frequently of interest to characterize the relationship between risk of acquisition of infection with a pathogen and a biomarker previously measuring for an immune response against that pathogen induced by prior infection and/or vaccination. It is common to conduct inference within a causal framework, wherein we desire to make inferences about the counterfactual probability of survival through a given time point, at any given exposure level. To determine whether a causal effect is present, one can assess if this quantity differs by exposure level. Recent work shows that, under typical causal assumptions, summaries of the counterfactual survival distribution are identifiable. Moreover, when the treatment is multi-level, these summaries are also pathwise differentiable in a nonparametric probability model, making it possible to construct estimators thereof that are unbiased and approximately normal. In cases where the treatment is continuous, the target estimand is no longer pathwise differentiable, rendering it difficult to construct well-behaved estimators without strong parametric assumptions. In this work, we extend beyond the traditional setting with multilevel interventions to develop approaches to nonparametric inference with a continuous exposure. We introduce methods for testing whether the counterfactual probability of survival time by a given time-point remains constant across the range of the continuous exposure levels. The performance of our proposed methods is evaluated via numerical studies, and we apply our method to data from a recent pair of efficacy trials of an HIV monoclonal antibody.