Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods.
- 1Sydney School of Public Health, University of Sydney, Sydney, Australia.
- 2MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
- 3Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
Abstract
Weak instruments are a well-known limitation for valid causal inference in Mendelian randomization studies. In the single exposure setting, weak instrument bias can be mitigated by selecting genetic instruments which are strongly associated with the exposure according to p-value and/or F-statistic thresholds. However, in the multi-exposure setting, genetic instruments may be strongly associated with an exposure but weakly associated with it conditional on all other exposures in the analysis. It is therefore more difficult to guarantee conditionally strong instruments in multivariable Mendelian randomization. Weak instrument bias can be mitigated using modeling approaches; however, there are fewer methods for doing this in the multivariable case compared with the single exposure case. In this paper, we consider a method for mitigating weak instrument bias in multivariable Mendelian randomization using a Bayesian framework: MVMR-Pony. We compare this method with existing frequentist methods. We show using simulation studies that the MVMR-Pony method outperforms the frequentist approaches with respect to bias, coverage, Type I error rates, and power, across settings where weak instrument bias arises due to correlated genetic effects, measurement error, and mediation.
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BibTeX
@article{grant2026multivariable,
title = {{Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods.}},
author = {Andrew J Grant and Ashish Patel and Stephen Burgess},
journal = {Statistics in medicine},
year = {2026},
month = oct,
doi = {10.1002/sim.70788},
url = {https://europepmc.org/article/MED/42894289},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods.
AU - Andrew J Grant
AU - Ashish Patel
AU - Stephen Burgess
PY - 2026
DA - 2026/10/01
JO - Statistics in medicine
DO - 10.1002/sim.70788
UR - https://europepmc.org/article/MED/42894289
AB - Weak instruments are a well-known limitation for valid causal inference in Mendelian randomization studies. In the single exposure setting, weak instrument bias can be mitigated by selecting genetic instruments which are strongly associated with the exposure according to p-value and/or F-statistic thresholds. However, in the multi-exposure setting, genetic instruments may be strongly associated with an exposure but weakly associated with it conditional on all other exposures in the analysis. It is therefore more difficult to guarantee conditionally strong instruments in multivariable Mendelian randomization. Weak instrument bias can be mitigated using modeling approaches; however, there are fewer methods for doing this in the multivariable case compared with the single exposure case. In this paper, we consider a method for mitigating weak instrument bias in multivariable Mendelian randomization using a Bayesian framework: MVMR-Pony. We compare this method with existing frequentist methods. We show using simulation studies that the MVMR-Pony method outperforms the frequentist approaches with respect to bias, coverage, Type I error rates, and power, across settings where weak instrument bias arises due to correlated genetic effects, measurement error, and mediation.
SN - 0277-6715
SN - 1097-0258
ER -