ArticleStatistics in medicine2014
Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Exploration of the association between 91 inflammatory proteins and immune thrombocytopenia: a two-sample Mendelian randomization analysis.International journal of hematology · 2025Article
- Genetic support for the causal association between 91 circulating inflammatory proteins and atopic dermatitis: A two-sample Mendelian randomization trial.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Common and RareInternational journal of molecular sciences · 2022Article
- GIVE statistic for goodness of fit in instrumental variables models with application to COVID data.Scientific reports · 2022Article
- Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects.American journal of epidemiology · 2018Article
- Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants.Epidemiology (Cambridge, Mass.) · 2017Article
- Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods.Genetic epidemiology · 2016Article
- 3-Hydroxy-3-methylglutaryl-CoA reductase variants strongly associated with low-density lipoprotein cholesterol levels and diabetes mellitus risk in a Taiwanese population: A Mendelian randomization study.Tzu chi medical journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerable work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent because of the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed.
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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.