ArticleStatistics in biosciences2021
A general approach to sensitivity analysis for Mendelian randomization.
Article in Statistics in biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Gut microbiota, lipid metabolism, and PCOS: A Mendelian randomization and mediation analysis.Medicine · 2026Article
- Discovery of circadian rhythm-related hub genes in acute myocardial infarction: A two-sample Mendelian randomization study.Medicine · 2026Article
- Genetic Evidence Links Gestational Diabetes Mellitus to Increased ER-Negative Breast Cancer Risk Through a Mendelian Randomization Analysis.International journal of endocrinology · 2026Article
- Investigating the causal role of serum metabolites in substance use disorder risk: a study integrating Mendelian randomization and synthesis analysis.Discover mental health · 2025Article
- Predicting dementia in people with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Exploring Therapeutic Potential of Bi-Qi Capsules in Treatment of Gout by Discovering Crucial Drug Targets.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Genetic relationship between ageing and coronary heart disease: a Mendelian randomization study.European geriatric medicine · 2024Article
- Peripheral Inflammatory Factors and Acute Myocardial Infarction Risk: A Mendelian Randomization Study.Global heart · 2023Article
- Exploring the causality between ankylosing spondylitis and atrial fibrillation: A two-sample Mendelian randomization study.Frontiers in genetics · 2022Article
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Authors and funding
2 authors.
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Abstract
Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.
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