Literature DB >> 22247045

Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions.

M Maria Glymour1, Eric J Tchetgen Tchetgen, James M Robins.   

Abstract

As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.

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Year:  2012        PMID: 22247045      PMCID: PMC3366596          DOI: 10.1093/aje/kwr323

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  25 in total

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2.  Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

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Journal:  Int J Epidemiol       Date:  2010-09-02       Impact factor: 7.196

3.  Instruments for causal inference: an epidemiologist's dream?

Authors:  Miguel A Hernán; James M Robins
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4.  Does greater adiposity increase blood pressure and hypertension risk?: Mendelian randomization using the FTO/MC4R genotype.

Authors:  Nicholas J Timpson; Roger Harbord; George Davey Smith; Jeppe Zacho; Anne Tybjaerg-Hansen; Børge G Nordestgaard
Journal:  Hypertension       Date:  2009-05-26       Impact factor: 10.190

5.  Biomarkers and cardiovascular disease: determining causality and quantifying contribution to risk assessment.

Authors:  Svati H Shah; James A de Lemos
Journal:  JAMA       Date:  2009-07-01       Impact factor: 56.272

6.  Random allocation in observational data: how small but robust effects could facilitate hypothesis-free causal inference.

Authors:  George Davey Smith
Journal:  Epidemiology       Date:  2011-07       Impact factor: 4.822

7.  Examining overweight and obesity as risk factors for common mental disorders using fat mass and obesity-associated (FTO) genotype-instrumented analysis: The Whitehall II Study, 1985-2004.

Authors:  Mika Kivimäki; Markus Jokela; Mark Hamer; John Geddes; Klaus Ebmeier; Meena Kumari; Archana Singh-Manoux; Aroon Hingorani; G David Batty
Journal:  Am J Epidemiol       Date:  2011-01-19       Impact factor: 4.897

8.  Using multiple genetic variants as instrumental variables for modifiable risk factors.

Authors:  Tom M Palmer; Debbie A Lawlor; Roger M Harbord; Nuala A Sheehan; Jon H Tobias; Nicholas J Timpson; George Davey Smith; Jonathan A C Sterne
Journal:  Stat Methods Med Res       Date:  2011-01-07       Impact factor: 3.021

9.  Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease--a Mendelian Randomisation study.

Authors:  Patrick Linsel-Nitschke; Anika Götz; Jeanette Erdmann; Ingrid Braenne; Peter Braund; Christian Hengstenberg; Klaus Stark; Marcus Fischer; Stefan Schreiber; Nour Eddine El Mokhtari; Arne Schaefer; Jürgen Schrezenmeir; Jürgen Schrezenmeier; Diana Rubin; Anke Hinney; Thomas Reinehr; Christian Roth; Jan Ortlepp; Peter Hanrath; Alistair S Hall; Massimo Mangino; Wolfgang Lieb; Claudia Lamina; Iris M Heid; Angela Doering; Christian Gieger; Annette Peters; Thomas Meitinger; H-Erich Wichmann; Inke R König; Andreas Ziegler; Florian Kronenberg; Nilesh J Samani; Heribert Schunkert
Journal:  PLoS One       Date:  2008-08-20       Impact factor: 3.240

10.  Mendelian randomisation and causal inference in observational epidemiology.

Authors:  Nuala A Sheehan; Vanessa Didelez; Paul R Burton; Martin D Tobin
Journal:  PLoS Med       Date:  2008-08-26       Impact factor: 11.069

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  117 in total

1.  Fasting Glucose and the Risk of Depressive Symptoms: Instrumental-Variable Regression in the Cardiovascular Risk in Young Finns Study.

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Journal:  Int J Behav Med       Date:  2017-12

2.  Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research.

Authors:  Steven D Pizer
Journal:  Health Serv Res       Date:  2015-08-21       Impact factor: 3.402

Review 3.  Methodological challenges in mendelian randomization.

Authors:  Tyler J VanderWeele; Eric J Tchetgen Tchetgen; Marilyn Cornelis; Peter Kraft
Journal:  Epidemiology       Date:  2014-05       Impact factor: 4.822

4.  Investigating the causal effect of vitamin D on serum adiponectin using a Mendelian randomization approach.

Authors:  L L N Husemoen; T Skaaby; T Martinussen; T Jørgensen; B H Thuesen; C Kistorp; J Jeppesen; J P Thyssen; M Meldgaard; P B Szecsi; M Fenger; A Linneberg
Journal:  Eur J Clin Nutr       Date:  2013-11-13       Impact factor: 4.016

5.  Do genetic risk scores for body mass index predict risk of phobic anxiety? Evidence for a shared genetic risk factor.

Authors:  S Walter; M M Glymour; K Koenen; L Liang; E J Tchetgen Tchetgen; M Cornelis; S-C Chang; M Rewak; E Rimm; I Kawachi; L D Kubzansky
Journal:  Psychol Med       Date:  2014-05-28       Impact factor: 7.723

6.  The Geographic Distribution of Genetic Risk as Compared to Social Risk for Chronic Diseases in the United States.

Authors:  David H Rehkopf; Benjamin W Domingue; Mark R Cullen
Journal:  Biodemography Soc Biol       Date:  2016

7.  Toward a clearer portrayal of confounding bias in instrumental variable applications.

Authors:  John W Jackson; Sonja A Swanson
Journal:  Epidemiology       Date:  2015-07       Impact factor: 4.822

Review 8.  The contribution of genetic and environmental factors to the duration of pregnancy.

Authors:  Timothy P York; Lindon J Eaves; Michael C Neale; Jerome F Strauss
Journal:  Am J Obstet Gynecol       Date:  2013-10-02       Impact factor: 8.661

9.  Instrumental variable analysis of multiplicative models with potentially invalid instruments.

Authors:  Michelle Shardell; Luigi Ferrucci
Journal:  Stat Med       Date:  2016-08-16       Impact factor: 2.373

10.  Diabetic Phenotypes and Late-Life Dementia Risk: A Mechanism-specific Mendelian Randomization Study.

Authors:  Stefan Walter; Jessica R Marden; Laura D Kubzansky; Elizabeth R Mayeda; Paul K Crane; Shun-Chiao Chang; Marilyn Cornelis; David H Rehkopf; Shubhabrata Mukherjee; M Maria Glymour
Journal:  Alzheimer Dis Assoc Disord       Date:  2016 Jan-Mar       Impact factor: 2.703

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