Jack Bowden
Jack Bowden
Programme Leader at MRC Integrative Epidemiology Unit, University of Bristol
Verified email at bristol.ac.uk - Homepage
TitleCited byYear
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
J Bowden, GD Smith, S Burgess, Unit
International Journal of Epidemiology 44, 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
Methods to estimate the between‐study variance and its uncertainty in meta‐analysis
AA Veroniki, D Jackson, W Viechtbauer, R Bender, J Bowden, G Knapp, ...
Research synthesis methods 7 (1), 55-79, 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
Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics
J Bowden, JF Tierney, AJ Copas, S Burdett
BMC medical research methodology 11 (1), 41, 2011
Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the statistic
J Bowden, F Del Greco M, C Minelli, G Davey Smith, NA Sheehan, ...
International journal of epidemiology 45 (6), 1961-1974, 2016
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
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
FP Hartwig, G Davey Smith, J Bowden
International journal of epidemiology 46 (6), 1985-1998, 2017
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
FP Hartwig, G Davey Smith, J Bowden
International journal of epidemiology 46 (6), 1985-1998, 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
Wolters Kluwer, 2016
Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies
PC Haycock, S Burgess, KH Wade, J Bowden, C Relton, GD Smith
The American journal of clinical nutrition 103 (4), 965-978, 2016
A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization
J Bowden, M Del Greco, C Minelli, G Davey Smith, N Sheehan, ...
Statistics in medicine 36 (11), 1783-1802, 2017
Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits
JM Lane, J Liang, I Vlasac, SG Anderson, DA Bechtold, J Bowden, ...
Nature Genetics 49 (2), 274-281, 2017
How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts?
D Jackson, J Bowden, R Baker
Journal of Statistical Planning and Inference 140 (4), 961-970, 2010
Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges
SS Villar, J Bowden, J Wason
Statistical science: a review journal of the Institute of Mathematical …, 2015
On instrumental variables estimation of causal odds ratios
S Vansteelandt, J Bowden, M Babanezhad, E Goetghebeur
Statistical Science 26 (3), 403-422, 2011
MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations
G Hemani, J Zheng, KH Wade, C Laurin, B Elsworth, S Burgess, ...
bioRxiv, 078972, 2016
Unbiased Estimation of Selected Treatment Means in Two‐Stage Trials
J Bowden, E Glimm
Biometrical Journal 50 (4), 515-527, 2008
Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study
SH Gage, HJ Jones, S Burgess, J Bowden, GD Smith, S Zammit, ...
Psychological medicine 47 (5), 971-980, 2017
BMI as a modifiable risk factor for type 2 diabetes: refining and understanding causal estimates using Mendelian randomization
LJ Corbin, RC Richmond, KH Wade, S Burgess, J Bowden, GD Smith, ...
Diabetes 65 (10), 3002-3007, 2016
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