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Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

Katherine L Hoffman1, Diego Salazar-Barreto2, Nicholas T Williams3

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This tutorial introduces longitudinal modified treatment policies for causal inference, generalizing parameters like average treatment effect. This flexible method handles complex data, including time-varying factors and various outcomes, with practical examples and R code available.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Causal inference methods are crucial for understanding treatment effects in observational studies.
  • Existing methods like static and dynamic interventions have limitations with complex time-varying data.
  • Novel parameters and generalizations are needed for robust causal effect estimation.

Purpose of the Study:

  • To introduce and explain the methodology of longitudinal modified treatment policies (LMTPs).
  • To demonstrate the mathematical formalization, identification, and estimation capabilities of LMTPs.
  • To illustrate practical applications and provide estimation strategies for LMTPs.

Main Methods:

  • Utilizes longitudinal modified treatment policies for causal inference.
  • Applies to diverse exposures (binary, multivariate, continuous) and outcomes (survival, binary, continuous).
  • Accommodates time-varying treatments, confounders, competing risks, and loss to follow-up.

Main Results:

  • LMTPs generalize parameters such as the average treatment effect.
  • They allow for the definition of alternative estimands with potentially more satisfiable positivity assumptions than static interventions.
  • The tutorial provides examples, including estimating the effect of delayed intubation on COVID-19 mortality.

Conclusions:

  • LMTPs offer a flexible and powerful framework for causal inference with complex longitudinal data.
  • The methodology extends existing intervention strategies and allows for estimation of novel causal parameters.
  • The open-source R package lmtp facilitates the practical application of LMTPs.