Stephen Burgess
Stephen Burgess
University of Cambridge, MRC Biostatistics Unit
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
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
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
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
Mendelian randomization analysis with multiple genetic variants using summarized data
S Burgess, A Butterworth, SG Thompson
Genetic epidemiology 37 (7), 658-665, 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
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
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
Association of cardiometabolic multimorbidity with mortality
E Di Angelantonio, S Kaptoge, D Wormser, P Willeit, AS Butterworth, ...
Jama 314 (1), 52-60, 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
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
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
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
Interpreting findings from Mendelian randomization using the MR-Egger method
S Burgess, SG Thompson
European journal of epidemiology 32 (5), 377-389, 2017
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
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
Bias due to participant overlap in two‐sample Mendelian randomization
S Burgess, NM Davies, SG Thompson
Genetic epidemiology 40 (7), 597-608, 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
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
Use of allele scores as instrumental variables for Mendelian randomization
S Burgess, SG Thompson
International journal of epidemiology 42 (4), 1134-1144, 2013
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
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