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Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression

Daniel Almirall1, Beth Ann Griffin, Daniel F McCaffrey

  • 1Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, U.S.A.

Statistics in Medicine
|July 23, 2013
PubMed
Summary

This study introduces a new method for analyzing how time-varying factors influence causal effects over time using observational data. The approach helps understand moderated time-varying causal effects, even with complex confounding variables.

Keywords:
effect modificationinverse-probability-of-treatment weightingtime-varying confoundingtime-varying covariatestime-varying exposuretime-varying treatment

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Area of Science:

  • Causal inference
  • Longitudinal data analysis
  • Epidemiology

Background:

  • Analyzing time-varying causal effect moderation with observational longitudinal data presents challenges due to time-varying treatments, moderators, and confounders.
  • Existing methods may not fully account for complex time-varying confounding structures.
  • Distinguishing between time-varying moderators and confounders is crucial for accurate causal effect estimation.

Purpose of the Study:

  • To develop and present an accessible estimator for moderated time-varying causal effects within the structural nested mean model (SNMM) framework.
  • To extend causal effect estimation to scenarios with auxiliary time-varying confounders beyond candidate moderators.
  • To illustrate the application of the proposed methodology using a case study on substance use.

Main Methods:

  • Utilized the structural nested mean model (SNMM) to define moderated time-varying causal effects.
  • Proposed an estimator combining a regression-with-residuals (RR) approach with inverse-probability-of-treatment weighting (IPTW), termed IPTW+RR.
  • Conducted a simulation experiment to compare IPTW+RR with traditional regression and evaluate standard error estimators.

Main Results:

  • The IPTW+RR approach provides valid estimators for moderated time-varying causal effects in the SNMM, accommodating additional time-varying confounders.
  • Simulation results demonstrated the performance of the IPTW+RR method.
  • The study clarified the conceptual and analytical distinctions between time-varying moderators and confounders.

Conclusions:

  • The IPTW+RR method offers a practical solution for estimating moderated time-varying causal effects in complex longitudinal observational studies.
  • The methodology is applicable to various fields, including public health and social sciences, where time-varying exposures and outcomes are common.
  • The case study successfully illustrated the utility of the approach in examining substance use moderation.