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Using instrumental variables to estimate the attributable fraction.

Elisabeth Dahlqwist1, Zoltán Kutalik2, Arvid Sjölander1

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Statistical Methods in Medical Research
|October 24, 2019
PubMed
Summary

Estimating the attributable fraction for public health requires addressing unmeasured confounding. This study applies instrumental variable methods, including Mendelian randomization, to assess the causal link between low educational qualifications and coronary heart disease.

Keywords:
Attributable fractionG-estimatorMendelian randomizationbinary outcomescausal inferencecoronary heart diseaseeducational qualificationsinstrumental variabletwo-stage estimatorunmeasured confounding

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetics

Background:

  • Public health interventions require accurate burden of disease estimates.
  • The attributable fraction quantifies preventable cases due to risk factors.
  • Unmeasured confounding poses a challenge for estimating attributable fractions from observational data.

Purpose of the Study:

  • To present instrumental variable methods for estimating the attributable fraction.
  • To assess the causal effect of low educational qualifications on coronary heart disease using Mendelian randomization.
  • To compare the performance of two-stage and G-estimators for attributable fraction estimation.

Main Methods:

  • Utilized instrumental variable analysis, specifically Mendelian randomization.
  • Employed two-stage and G-estimators to estimate causal risk ratios and odds ratios.
  • Analyzed data from the UK Biobank, investigating educational qualifications and coronary heart disease.

Main Results:

  • Estimated causal risk and odds ratios for low educational qualifications as a risk factor for coronary heart disease.
  • Compared attributable fractions derived from two-stage and G-estimators.
  • Evaluated the plausibility of causal conclusions and tested alternative genetic instrumental variables.

Conclusions:

  • Instrumental variable methods offer a solution to unmeasured confounding in attributable fraction estimation.
  • Mendelian randomization provides insights into the causal relationship between education and coronary heart disease.
  • The study highlights the importance of robust methods for causal inference in public health research.