J. Thompson

发表

Bayesian Analysis in Stata using WinBUGS

J. Thompson, John Thompson, 2006 .

Bayesian implementation of a genetic model‐free approach to the meta‐analysis of genetic association studies

K. Abrams, P. Lambert, C. Minelli, 2005, Statistics in medicine.

Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up

G. Parmigiani, G. Borsani, M. Boehnke, 2013, Genetic epidemiology.

On the choice of parameterisation and priors for the Bayesian analyses of Mendelian randomisation studies

N A Sheehan, J R Thompson, V Didelez, 2012, Statistics in medicine.

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects

John R Thompson, Duncan C Thomas, Christian X Weichenberger, 2017, Genetic epidemiology.

Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression

N. Sheehan, G. Davey Smith, J. Bowden, 2018, International journal of epidemiology.

Bayesian hierarchical meta‐analytic methods for modeling surrogate relationships that vary across treatment classes using aggregate data

K. Abrams, S. Bujkiewicz, O. Ciani, 2020, Statistics in medicine.

Using pseudo‐data to correct for publication bias in meta‐analysis

Jack Bowden, John R Thompson, Paul Burton, 2006, Statistics in medicine.

Bayesian Analysis in Stata with WinBUGS

John M. Thompson, Tom M. Palmer, Santiago G. Moreno, 2006 .

Importance of Different Types of Prior Knowledge in Selecting Genome‐Wide Findings for Follow‐Up

G. Parmigiani, G. Borsani, M. Boehnke, 2013, Genetic epidemiology.

BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model

C. Minelli, M. Blangiardo, F. Elfadaly, 2021, International Journal of Epidemiology.