Stephen Burgess

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Dipender GillImperial College LondonVerified email at imperial.ac.uk
Susanna C. LarssonUppsala UniversityVerified email at uu.se
Amy MasonBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and PrimaryVerified email at medschl.cam.ac.uk
George Davey SmithUniversity of BristolVerified email at bristol.ac.uk
Adam ButterworthProfessor of Molecular EpidemiologyVerified email at medschl.cam.ac.uk
Jack BowdenProfessor of Biomedical Data Science, University of Exeter and Senior Director, Novo Nordisk OxfordVerified email at exeter.ac.uk
Shuai YuanPerelman School of Medicine at the University of PennsylvaniaVerified email at pennmedicine.upenn.edu
John DaneshUniversity of CambridgeVerified email at medschl.cam.ac.uk
Siddhartha KarAssociate Professor, University of CambridgeVerified email at cam.ac.uk
Neil M DaviesUniversity College London, Division of Psychiatry, Department of Statistical ScienceVerified email at ucl.ac.uk
Caroline ReltonProfessor, University of BristolVerified email at bristol.ac.uk
James YarmolinskyImperial College LondonVerified email at imperial.ac.uk
Angela WoodUniversity of CambridgeVerified email at medschl.cam.ac.uk
Christopher N FoleyUniversity of CambridgeVerified email at mrc-bsu.cam.ac.uk
Claudia LangenbergBerlin Institute of Health at Charité, Germany & MRC Epidemiology Unit, University of Cambridge, UKVerified email at charite.de
Michaelsson, KarlProfessor of Medical Epidemiology, Uppsala UniversityVerified email at surgsci.uu.se
Emanuele Di AngelantonioUniversity of Cambridge / Human TechnopoleVerified email at medschl.cam.ac.uk
Nicholas WarehamDirector, MRC Epidemiology Unit, University of CambridgeVerified email at mrc-epid.cam.ac.uk
Frank DudbridgeUniversity of LeicesterVerified email at leicester.ac.uk
Michael V Holmes23andMeVerified email at 23andme.com

Stephen Burgess

University of Cambridge, MRC Biostatistics Unit

Verified email at medschl.cam.ac.uk - Homepage

Mendelian randomization instrumental variables causal inference


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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression J Bowden, G Davey Smith, S Burgess International journal of epidemiology 44 (2), 512-525, 2015	7646	2015
Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator J Bowden, G Davey Smith, PC Haycock, S Burgess Genetic epidemiology 40 (4), 304-314, 2016	6958	2016
The MR-Base platform supports systematic causal inference across the human phenome G Hemani, J Zheng, B Elsworth, KH Wade, V Haberland, D Baird, ... elife 7, e34408, 2018	6104	2018
Mendelian randomization analysis with multiple genetic variants using summarized data S Burgess, A Butterworth, SG Thompson Genetic epidemiology 37 (7), 658-665, 2013	4686	2013
Interpreting findings from Mendelian randomization using the MR-Egger method S Burgess, SG Thompson European journal of epidemiology 32, 377-389, 2017	3493	2017
Avoiding bias from weak instruments in Mendelian randomization studies S Burgess, SG Thompson, Crp Chd Genetics Collaboration International journal of epidemiology 40 (3), 755-764, 2011	2782	2011
Genomic atlas of the human plasma proteome BB Sun, JC Maranville, JE Peters, D Stacey, JR Staley, J Blackshaw, ... Nature 558 (7708), 73-79, 2018	1795	2018
PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations MA Kamat, JA Blackshaw, R Young, P Surendran, S Burgess, J Danesh, ... Bioinformatics 35 (22), 4851-4853, 2019	1779	2019
Guidelines for performing Mendelian randomization investigations: update for summer 2023 S Burgess, GD Smith, NM Davies, F Dudbridge, D Gill, MM Glymour, ... Wellcome open research 4, 186, 2023	1771	2023
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data OO Yavorska, S Burgess International journal of epidemiology 46 (6), 1734-1739, 2017	1707	2017
Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants S Burgess, J Bowden, T Fall, E Ingelsson, SG Thompson Epidemiology 28 (1), 30-42, 2017	1550	2017
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors S Burgess, RA Scott, NJ Timpson, G Davey Smith, SG Thompson, ... European journal of epidemiology 30, 543-552, 2015	1435	2015
A review of instrumental variable estimators for Mendelian randomization S Burgess, DS Small, SG Thompson Statistical methods in medical research 26 (5), 2333-2355, 2017	1424	2017
Bias due to participant overlap in two‐sample Mendelian randomization S Burgess, NM Davies, SG Thompson Genetic epidemiology 40 (7), 597-608, 2016	1398	2016
Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies AM Wood, S Kaptoge, AS Butterworth, P Willeit, S Warnakula, T Bolton, ... The Lancet 391 (10129), 1513-1523, 2018	1371	2018
PhenoScanner: a database of human genotype–phenotype associations JR Staley, J Blackshaw, MA Kamat, S Ellis, P Surendran, BB Sun, ... Bioinformatics 32 (20), 3207-3209, 2016	1370	2016
Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects S Burgess, SG Thompson American journal of epidemiology 181 (4), 251-260, 2015	1344	2015
Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators BL Pierce, S Burgess American journal of epidemiology 178 (7), 1177-1184, 2013	1224	2013
Association of cardiometabolic multimorbidity with mortality E Di Angelantonio, S Kaptoge, D Wormser, P Willeit, AS Butterworth, ... Jama 314 (1), 52-60, 2015	995	2015
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods S Burgess, F Dudbridge, SG Thompson Statistics in medicine 35 (11), 1880-1906, 2016	939	2016

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