Madigan, David

Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science

Statistical Methods and Bayesian InferenceStatistical Methods and Inference

Cited by 4,118

Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window

Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association

Bayesian Modeling and Causal InferenceStatistical Methods and Inference

Cited by 1,205

Bayesian Graphical Models for Discrete Data

Madigan, D., York, J., & Allard, D. (1995) · International Statistical Review / Revue Internationale de Statistique

Bayesian Modeling and Causal InferenceMarkov Chains and Monte Carlo Methods

Cited by 1,090

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare

Cited by 766

A characterization of Markov equivalence classes for acyclic digraphs

Andersson, S. A., Madigan, D., & Perlman, M. D. (1997) · The Annals of Statistics

Bayesian Modeling and Causal InferenceStatistical Methods and Inference

Cited by 425

Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis

Suchard, M. A., Schuemie, M. J., Krumholz, H. M., You, S. C., Chen, R., Pratt, N., Reich, C. G., Duke, J., Madigan, D., Hripcsak, G., et al. (2019) · The Lancet

Blood Pressure and Hypertension StudiesCardiovascular Health and Disease Prevention

Cited by 359

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak, G., Ryan, P. B., Duke, J. D., Shah, N. H., Park, R. W., Huser, V., Suchard, M. A., Schuemie, M. J., DeFalco, F. J., Perotte, A., et al. (2016) · Proceedings of the National Academy of Sciences

Medical Coding and Health InformationMachine Learning in Healthcare

Cited by 326

Interpreting observational studies: why empirical calibration is needed to correct p‐values

Schuemie, M. J., Ryan, P. B., DuMouchel, W., Suchard, M. A., & Madigan, D. (2014) · Statistics in Medicine

Statistical Methods in EpidemiologyAdvanced Causal Inference Techniques

Cited by 234

Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke

Volinsky, C. T., Madigan, D., Raftery, A. E., & Kronmal, R. A. (1997) · Journal of the Royal Statistical Society Series C: Applied Statistics

Statistical Methods and InferenceStatistical Methods and Bayesian Inference

Cited by 226

Comparative First-Line Effectiveness and Safety of ACE (Angiotensin-Converting Enzyme) Inhibitors and Angiotensin Receptor Blockers: A Multinational Cohort Study

Chen, R., Suchard, M. A., Krumholz, H. M., Schuemie, M. J., Shea, S., Duke, J., Pratt, N., Reich, C. G., Madigan, D., You, S. C., et al. (2021) · Hypertension

Blood Pressure and Hypertension StudiesRenin-Angiotensin System Studies

Cited by 190

Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data

Schuemie, M. J., Hripcsak, G., Ryan, P. B., Madigan, D., & Suchard, M. A. (2018) · Proceedings of the National Academy of Sciences

Advanced Causal Inference TechniquesStatistical Methods in Epidemiology

Cited by 163

Improving reproducibility by using high-throughput observational studies with empirical calibration

Schuemie, M. J., Ryan, P. B., Hripcsak, G., Madigan, D., & Suchard, M. A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Meta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Cited by 74

Eliciting prior information to enhance the predictive performance of bayesian graphical models

Madigan, D., Gavrin, J., & Raftery, A. E. (1995) · Communications in Statistics - Theory and Methods

Bayesian Modeling and Causal InferenceStatistical Methods and Bayesian Inference

Cited by 60

Robust empirical calibration of p‐values using observational data

Schuemie, M. J., Hripcsak, G., Ryan, P. B., Madigan, D., & Suchard, M. A. (2016) · Statistics in Medicine

Advanced Causal Inference TechniquesStatistical Methods in Epidemiology

Cited by 53

Birth Defects Registered by Double Sampling: A Bayesian Approach Incorporating Covariates and Model Uncertainty

York, J., Madigan, D., Heuch, I., & Lie, R. T. (1995) · Applied Statistics

Statistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Cited by 52

Combining cox regressions across a heterogeneous distributed research network facing small and zero counts

Schuemie, M. J., Chen, Y., Madigan, D., & Suchard, M. A. (2022) · Statistical Methods in Medical Research

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 20