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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Simulating from marginal structural models with time-dependent confounding.

W G Havercroft1, V Didelez

  • 1School of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK. w.havercroft@bristol.ac.uk

Statistics in Medicine
|July 25, 2012
PubMed
Summary
This summary is machine-generated.

Simulating longitudinal data with time-dependent confounding from marginal structural models (MSMs) is complex. This study introduces a valid data-generating process for survival outcomes, aiding in the evaluation of confounding adjustment methods.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Marginal structural models (MSMs) are used for causal inference in longitudinal studies.
  • Time-dependent confounding presents a significant challenge in analyzing survival outcomes.
  • Simulating realistic longitudinal data with time-dependent confounding is not straightforward.

Purpose of the Study:

  • To propose a method for simulating longitudinal data from a general marginal structural model (MSM).
  • To ensure the simulated data accurately reflect time-dependent confounding in survival outcomes.
  • To provide a tool for evaluating methods that adjust for time-dependent confounding.

Main Methods:

  • Utilized the relationship between directed acyclic graphs (DAGs) and MSMs.
  • Developed and mathematically proved a novel data-generating process.
  • Designed a simulation study emulating real-world longitudinal cohort studies (e.g., Swiss HIV Cohort Study).

Main Results:

  • The proposed data-generating process is valid and satisfies the requirements for simulating MSMs with time-dependent confounding.
  • The methodology facilitates a deeper understanding of MSM interpretation.
  • The simulation study allows for the comparison of different methods for adjusting time-dependent covariates.

Conclusions:

  • The developed data-generating process offers a reliable approach for simulating complex longitudinal data.
  • This method is crucial for assessing the performance of statistical techniques designed to handle time-dependent confounding.
  • The findings support the robust evaluation of causal inference methods in observational studies.