ArticleBiometrika2025
A More Robust Approach to Multivariable Mendelian Randomization.
Article in Biometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.Briefings in bioinformatics · 2026Article
- A flexible framework for robust and efficient Mendelian randomization with debiasing.Briefings in bioinformatics · 2026Article
- Early‑life head circumference is causally associated with adult executive function through cortical surface area expansion.Developmental cognitive neuroscience · 2026Article
- Comparison of methods for assessing effects of risk factors on disease progression in Mendelian randomization under index event bias.American journal of human genetics · 2026Article
- MRBEE-TL: improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning.Genome biology · 2026Article
- Exploring the Relationship Between Caffeine Consumption, Caffeine Metabolism, and Sleep Behaviours: A Mendelian Randomisation Study.Journal of sleep research · 2026Article
- Plasma proteomics and incident coronary heart disease.Communications medicine · 2026Article
- A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias.Briefings in bioinformatics · 2025Article
- Estimating Causal Effects on a Disease Progression Trait Using Bivariate Mendelian Randomisation.Genetic epidemiology · 2025Article
- Mediation-adjusted multivariable Mendelian randomisation study identified novel metabolites related to mental health.BMJ mental health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, MVMR often faces greater challenges with many weak instruments, which can lead to bias not necessarily toward zero and inflation of type I errors. In this work, we introduce a new asymptotic regime that allows exposures to have varying degrees of instrument strength, providing a more accurate theoretical framework for studying MVMR estimators. Under this regime, our analysis of the widely used multivariable inverse-variance weighted method shows that it is often biased and tends to produce misleadingly narrow confidence intervals in the presence of many weak instruments. To address this, we propose a simple, closed-form modification to the multivariable inverse-variance weighted estimator to reduce bias from weak instruments, and additionally introduce a novel spectral regularization technique to improve finite-sample performance. We show that the resulting spectral-regularized estimator remains consistent and asymptotically normal under many weak instruments. Through simulations and real data applications, we demonstrate that our proposed estimator and asymptotic framework can enhance the robustness of MVMR analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.