Stephen Burgess

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George Davey SmithUniversity of BristolVerified email at bristol.ac.uk
Jack BowdenProfessor of Biomedical Data Science, University of ExeterVerified email at exeter.ac.uk
Susanna C. LarssonKarolinska InstitutetVerified email at ki.se
Adam ButterworthReader in Molecular EpidemiologyVerified email at medschl.cam.ac.uk
Amy MasonUniversity of CambridgeVerified email at medschl.cam.ac.uk
Verena ZuberImperial College LondonVerified email at imperial.ac.uk
Caroline ReltonProfessor, University of BristolVerified email at bristol.ac.uk
Nicholas John TimpsonUniversity of BristolVerified email at bristol.ac.uk
Neil M DaviesMRC Integrative Epidemiology Unit (IEU) at the University of BristolVerified email at bristol.ac.uk
John DaneshUniversity of CambridgeVerified email at medschl.cam.ac.uk
James R StaleySenior Computational Genetics Scientist, UCBVerified email at bristol.ac.uk
Michaelsson, KarlProfessor of Medical Epidemiology, Uppsala UniversityVerified email at surgsci.uu.se
Frank DudbridgeUniversity of LeicesterVerified email at leicester.ac.uk
Nicholas WarehamDirector, MRC Epidemiology Unit, University of CambridgeVerified email at mrc-epid.cam.ac.uk
Apostolos GkatzionisSenior Research Associate, MRC Integrative Epidemiology Unit, University of BristolVerified email at bristol.ac.uk
Felix DayMRC Epidemiology UnitVerified email at mrc-epid.cam.ac.uk
Robert A ScottInvestigator ScientistVerified email at mrc-epid.cam.ac.uk
Benjamin ElsworthUniversity of BristolVerified email at bristol.ac.uk
Stephen SharpSenior Statistician, MRC Epidemiology Unit, University of CambridgeVerified email at mrc-epid.cam.ac.uk
Michael V HolmesClinical Trial Service Unit and Epidemiological Studies Unit (CTSU)Verified email at ndph.ox.ac.uk

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	1570	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	1165	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	1006*	2018
Mendelian randomization analysis with multiple genetic variants using summarized data S Burgess, A Butterworth, SG Thompson Genetic epidemiology 37 (7), 658-665, 2013	933	2013
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	552	2018
Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data F Wensley, P Gao, S Burgess, S Kaptoge, E Di Angelantonio, T Shah, ... BMJ: British Medical Journal 342, d548, 2011	549	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	439	2018
Association of cardiometabolic multimorbidity with mortality E Di Angelantonio, S Kaptoge, D Wormser, P Willeit, AS Butterworth, ... Jama 314 (1), 52-60, 2015	429	2015
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors S Burgess, RA Scott, NJ Timpson, GD Smith, SG Thompson European journal of epidemiology 30 (7), 543-552, 2015	414	2015
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	404	2016
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	366	2017
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	353	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	349	2013
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	337	2011
Interpreting findings from Mendelian randomization using the MR-Egger method S Burgess, SG Thompson European journal of epidemiology 32 (5), 377-389, 2017	325	2017
Use of allele scores as instrumental variables for Mendelian randomization S Burgess, SG Thompson International journal of epidemiology 42 (4), 1134-1144, 2013	308	2013
Bias due to participant overlap in two‐sample Mendelian randomization S Burgess, NM Davies, SG Thompson Genetic epidemiology 40 (7), 597-608, 2016	306	2016
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 (Cambridge, Mass.) 28 (1), 30, 2017	290	2017
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	279	2017
Association between telomere length and risk of cancer and non-neoplastic diseases: a Mendelian randomization study PC Haycock, S Burgess, A Nounu, J Zheng, GN Okoli, J Bowden, ... JAMA oncology 3 (5), 636-651, 2017	255	2017

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