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Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses
Tom M Palmer1, John R Thompson, Martin D Tobin
1Department of Health Sciences, University of Leicester, UK.
International Journal of Epidemiology
|May 9, 2008
Summary
Mendelian randomization studies with binary outcomes can have biased estimates. An adjusted instrumental variable (IV) estimator often minimizes bias, but sensitivity analyses are crucial due to potential confounding.
Area of Science:
- Genetic epidemiology
- Biostatistics
- Statistical genetics
Background:
- Mendelian randomization (MR) employs genetic variants as instrumental variables (IVs) to infer causal relationships between phenotypes and diseases.
- Classical IV analysis assumes linear relationships, which may not hold for binary disease outcomes modeled via logistic regression, potentially biasing MR estimates.
- Investigates the extent of bias in the phenotype-disease log odds ratio within MR studies when linearity assumptions are violated.
Purpose of the Study:
- To evaluate the bias in Mendelian randomization (MR) estimates for the phenotype-disease log odds ratio when analyzing binary disease outcomes.
- To compare the performance of different instrumental variable (IV) estimators under conditions of unmeasured confounding.
Main Methods:
- A simulation study was conducted to compare three estimators: direct, standard IV, and adjusted IV.
- Unmeasured confounding was incorporated into the simulations to mimic real-world scenarios.
- Formulae relating marginal and conditional estimates were used to verify simulation results.
Main Results:
- The direct estimator exhibited bias due to unmeasured confounding.
- The standard IV estimator showed attenuation towards the null hypothesis.
- The adjusted IV estimator generally had the least bias, though it demonstrated inflated Type I error rates with strong unmeasured confounding.
Conclusions:
- Bias in estimating the phenotype-disease log odds ratio in MR studies with binary outcomes can be significant.
- Sensitivity analyses are recommended to assess the impact of potential confounding on MR estimates.
- Careful consideration of estimator choice and sensitivity analysis is vital for reliable MR findings in binary trait analyses.
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