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Multiple Imputation to Account for Measurement Error in Marginal Structural Models.

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This study introduces a new method to correct for measurement errors in observational studies using marginal structural models. Accounting for smoking misclassification revealed its association with mortality in HIV patients not on therapy.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Marginal structural models (MSMs) are crucial for analyzing observational data.
  • Standard MSMs often assume error-free variable measurement, potentially biasing results.
  • This study addresses measurement error in MSMs, specifically for differential and nondifferential error types.

Purpose of the Study:

  • To develop and illustrate a method for incorporating measurement error correction into marginal structural models.
  • To investigate the joint effects of antiretroviral therapy (ART) and smoking on mortality in HIV patients, accounting for smoking misclassification.

Main Methods:

  • Utilized a US HIV cohort (12,290 patients, up to 5 years follow-up).
  • Employed multiple imputation to adjust for smoking status misclassification.
  • Compared standard MSM with inverse probability weighting to a model incorporating measurement error correction.

Main Results:

  • Standard analysis: current smoking showed no increased mortality risk.
  • Measurement error-adjusted analysis: current smoking without ART was linked to higher mortality (HR: 1.2, 95% CI: 0.6–2.3).
  • ART mitigated mortality risk for both smokers and non-smokers.

Conclusions:

  • Multiple imputation effectively corrects for measurement error in MSMs.
  • This approach enhances the reliability of causal inference from observational studies.
  • Accurate assessment of smoking's impact on mortality in HIV patients requires accounting for misclassification.