This tutorial discusses a recently developed methodology for causal inference based on longitudinal modified treatment policies (LMTPs). LMTPs generalize many commonly used parameters for causal inference including average treatment effects, and facilitate the mathematical formalization, identification, and estimation of many novel parameters. LMTPs apply to a wide variety of exposures, including binary, multivariate, and continuous, as well as interventions that result in violations of the positivity assumption. LMTPs can accommodate time-varying treatments and confounders, competing risks, loss-to-follow-up, as well as survival, binary, or continuous outcomes. This tutorial aims to illustrate several practical uses of the LMTP framework, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions which can be answered within the proposed framework. 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：本教程介绍了一种基于纵向修正处理策略（LMTPs）的因果推断新方法论。LMTPs 推广了许多常用的因果推断参数（包括平均处理效应），并促进了许多新参数的数学形式化、识别和估计。LMTPs 适用于多种暴露，包括二元、多变量和连续暴露，以及可能导致阳性假设违背的干预措施。LMTPs 能够处理时变治疗和混杂因素、竞争风险、失访，以及生存、二元或连续结局。本教程旨在阐述 LMTP 框架的若干实际用途，包括描述不同的估计策略及其相应的优缺点。我们提供了大量研究问题类型的示例，这些问题可以在所提出的框架内得到解答。我们深入探讨了其中一个示例——具体而言，估计延迟插管对危重症 COVID-19 患者死亡率的影响。我们展示了如何使用开源 R 包 lmtp 来估计效应，并在 https://github.com/kathoffman/lmtp-tutorial 上提供了相关代码。