Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as $\sqrt{n}$-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

翻译：纵向修正治疗策略（LMTP）作为一项新方法，近期被提出用于定义和估计依赖于治疗自然值的因果参数。LMTP是纵向研究因果推断领域的重要进展，它能非参数地定义并估计多个类别型、数值型或连续型暴露变量（在不同时间点测量）的联合效应。本文将LMTP方法论拓展至存在右删失和竞争风险的生存时间结局问题中。我们给出了识别性结论，并提出了利用灵活数据自适应回归技术缓解模型误设偏差的非参数局部有效估计量，同时保留了诸如$\sqrt{n}$一致性等重要渐近性质。我们以COVID-19住院患者为例，估计了插管时间对急性肾损伤的影响（其中其他原因导致的死亡被视为竞争事件）。