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.

翻译：在随机试验和观察性研究中，常需评估干预措施对时间-事件结局的影响程度，而该结局因右删失仅能被部分观测。例如，在传染病研究中，研究者常关注病原体感染风险与先前测量获得的免疫反应生物标志物（由既往感染和/或疫苗接种诱导）之间的关系。通常在因果框架内进行推断，旨在针对任意给定暴露水平，推断特定时间点的反事实生存概率。为判断因果效应是否存在，可评估该概率是否随暴露水平变化而变化。近期研究表明，在典型因果假设下，反事实生存分布的概括量是可识别的。此外，当治疗为多水平时，这些概括量在非参数概率模型中具有路径可微性，从而能够构建无偏且近似正态的估计量。当治疗为连续变量时，目标估计量不再具有路径可微性，导致难以在缺乏强参数假设的情况下构建性能良好的估计量。本研究突破多水平干预的传统框架，开发了适用于连续暴露的非参数推断方法。我们提出了一系列方法，用于检验特定时间点的反事实生存概率在连续暴露水平范围内是否保持恒定。通过数值研究评估了所提方法的性能，并将其应用于近期两项HIV单克隆抗体疗效试验的数据分析中。