Literature DB >> 33737984

A general approach to sensitivity analysis for Mendelian randomization.

Weiming Zhang1, Debashis Ghosh1.   

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.

Entities:  

Keywords:  causal inference; instrumental variable; sensitivity analysis; unmeasured confounding

Year:  2020        PMID: 33737984      PMCID: PMC7962901          DOI: 10.1007/s12561-020-09280-5

Source DB:  PubMed          Journal:  Stat Biosci        ISSN: 1867-1764


  30 in total

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10.  Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants.

Authors:  Stephen Burgess; Jack Bowden; Tove Fall; Erik Ingelsson; Simon G Thompson
Journal:  Epidemiology       Date:  2017-01       Impact factor: 4.822

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