Studying continuous, time-varying, and/or complex exposures using longitudinal modified treatment policies
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用纵向修正处理政策研究连续、时变及复杂暴露
- 研究问题
- 如何用纵向修正处理政策定义并估计复杂暴露的因果效应，同时处理时变处理与混杂、失访、竞争风险和多种结局？
- 方法与思路
- 教程介绍依赖处理自然取值的政策干预、相应识别与估计策略，并通过延迟插管对重症新冠患者死亡率的例子演示。
- 材料陈述的贡献
- 摘要称该框架推广多种常见效应参数，支持广泛暴露与结局类型，并比较估计策略及演示开源 R 包 lmtp 的使用。
- 阅读关注点
- 应核对政策如何依赖自然处理值、参数识别所需条件、正值性要求的具体比较，以及失访和竞争风险的处理方式。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；识别条件和案例结果未完整说明，较易满足正值性不代表识别自动成立。
原文摘要
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.