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This tutorial discusses methodology for causal inference using longitudinalmodified treatment policies. This method facilitates the mathematicalformalization, identification, and estimation of many novel parameters, andmathematically generalizes many commonly used parameters, such as the averagetreatment effect. Longitudinal modified treatment policies apply to a widevariety of exposures, including binary, multivariate, and continuous, and canaccommodate 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 staticand dynamic interventions to involve the natural value of treatment, and, likedynamic interventions, can be used to define alternative estimands with apositivity assumption that is more likely to be satisfied than estimandscorresponding to static interventions. This tutorial aims to illustrate severalpractical uses of the longitudinal modified treatment policy methodology,including describing different estimation strategies and their correspondingadvantages and disadvantages. We provide numerous examples of types of researchquestions which can be answered using longitudinal modified treatment policies.We go into more depth with one of these examples--specifically, estimating theeffect of delaying intubation on critically ill COVID-19 patients' mortality.We demonstrate the use of the open-source R package lmtp to estimate theeffects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

Studying continuous, time-varying, and/or complex exposures using longitudinal modified treatment policies