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Related Experiment Video

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Sensitivity Analysis in Nonrandomized Longitudinal Mediation Analysis.

Davood Tofighi1

  • 1Department of Psychology, University of New Mexico, Albuquerque, NM, United States.

Frontiers in Psychology
|December 23, 2021
PubMed
Summary

This study introduces a new method to test for omitted confounding variables in alcohol addiction research using latent growth curve mediation models. The findings highlight the critical need for sensitivity analysis to ensure the reliability of mediation results.

Keywords:
latent growth analysismediation analysisno omitted confounder assumptionsensitivity analysisstructural equation model (SEM)

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Area of Science:

  • Statistics
  • Psychology
  • Public Health

Background:

  • Mediation analysis assumes no omitted confounders, a critical but untestable assumption.
  • Latent Growth Curve Mediation Models (LGCMM) are common in alcohol addiction studies but lack sensitivity analysis for omitted confounders.
  • Existing sensitivity analysis techniques are not readily applicable to nonrandomized LGCMMs.

Purpose of the Study:

  • To extend the Correlated Augmented Mediation Sensitivity Analysis (CAMSA) technique to nonrandomized LGCMMs.
  • To address the lack of sensitivity analysis for omitted confounders in LGCMMs within alcohol addiction research.
  • To provide practical tools and guidance for assessing the robustness of mediation findings.

Main Methods:

  • Analytical results were derived to demonstrate how confounder correlations influence confounding bias.
  • Algorithms were developed to determine admissible values for confounder correlations.
  • Accessible R code was created within a Structural Equation Modeling (SEM) framework.
  • An empirical example was used to illustrate the application of the proposed method.

Main Results:

  • The study presents a novel extension of CAMSA for nonrandomized LGCMMs.
  • It provides a clear understanding of how confounder correlations impact bias in mediation models.
  • The developed algorithms and R code facilitate the practical implementation of sensitivity analysis.
  • An empirical example demonstrates the utility and application of the method.

Conclusions:

  • Sensitivity analysis is crucial for evaluating the robustness of mediation findings in nonrandomized LGCMMs.
  • The extended CAMSA technique offers a valuable tool for researchers in alcohol addiction and related fields.
  • Addressing the 'no omitted confounders' assumption is vital for valid causal inference.