This tutorial discusses methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters, and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss-to-follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment, and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions which can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples--specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

翻译：本教程系统阐述了利用纵向修正治疗策略进行因果推断的方法论。该方法可对多种新型参数进行数学形式化、识别与估计，并在数学上推广了平均处理效应等常用参数。纵向修正治疗策略适用于二元、多元及连续等各类暴露因素，能够处理时变治疗与混杂因素、竞争风险、失访现象，以及生存结局、二元结局或连续结局。纵向修正治疗策略可视为静态干预与动态干预的延伸，通过引入治疗的自然值来定义干预方案。与动态干预类似，该方法可在正性假设更易满足的条件下定义替代估计量，相比静态干预对应的估计量更具可行性。本教程旨在阐明纵向修正治疗策略方法的多种实际应用场景，包括介绍不同估计策略及其优缺点。我们提供了大量可通过纵向修正治疗策略解答的研究问题范例，并重点深入分析其中一个实例——具体估计延迟插管对重症COVID-19患者死亡率的影响。我们演示了开源R包lmtp的效应估计流程，相关代码见https://github.com/kathoffman/lmtp-tutorial。