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Structural nested mean models for assessing time-varying effect moderation.

Daniel Almirall1, Thomas Ten Have, Susan A Murphy

  • 1Center for Health Services Research in Primary Care, VA Medical Center, Durham, North Carolina 27705, USA. daniel.almirall@duke.edu

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Summary
This summary is machine-generated.

This study introduces intermediate causal effects for time-varying treatments and covariates in longitudinal research. It presents two estimators to assess moderation, aiding causal inference in complex health studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Assessing causal effect moderation is challenging with time-varying treatments and covariates.
  • Existing methods may not fully capture dynamic treatment effects influenced by evolving covariate histories.

Purpose of the Study:

  • To introduce and evaluate methods for estimating intermediate causal effects in longitudinal settings.
  • To assess the performance and bias-variance trade-offs of novel causal effect moderation estimators.

Main Methods:

  • Introduction of intermediate causal effects within Robins' structural nested mean model framework.
  • Development and presentation of a two-stage regression estimator.
  • Application of Robins' G-estimator for comparison.

Main Results:

  • A simulation study compared the performance of the two proposed estimators.
  • Findings shed light on small versus large sample performance and bias-variance trade-offs.
  • Methodology demonstrated using longitudinal depression study data.

Conclusions:

  • The proposed methods offer advancements in estimating time-varying causal effects and moderation.
  • The study provides valuable tools for causal inference in longitudinal health research.
  • Understanding intermediate causal effects is crucial for dynamic treatment regimes.