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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

An information criterion for marginal structural models.

Robert W Platt1, M Alan Brookhart, Stephen R Cole

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. robert.platt@mcgill.ca

Statistics in Medicine
|September 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new information criterion for specifying marginal structural models, improving causal effect estimation in observational studies. The method aids in selecting the best model for analyzing exposure effects, like breastfeeding or HIV treatment, on health outcomes.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Marginal structural models (MSMs) are semiparametric methods for estimating causal effects.
  • Parametric regression models are commonly used for MSM specification.
  • Model specification is crucial for accurate causal inference.

Purpose of the Study:

  • To outline strategies for MSM specification.
  • To address considerations for the functional form of exposure metrics.
  • To propose a novel information criterion for model selection.

Main Methods:

  • Development of a quasi-likelihood information criterion adapted from generalized estimating equations.
  • Evaluation of the criterion's properties via simulation.
  • Application of the method to two empirical examples (breastfeeding trial, HIV cohort).

Main Results:

  • The proposed information criterion offers a formal approach to comparing model fit for different MSM specifications.
  • The criterion was evaluated using simulations.
  • Illustrative examples demonstrated its practical application.

Conclusions:

  • Correct model specification is essential for valid inference in MSMs.
  • The proposed information criterion aids in selecting appropriate models for causal effect estimation.
  • This approach can be applied to diverse research questions in epidemiology and health research.