David Madigan

David Madigan, PhD
Professor of Statistics
Columbia University

David Madigan is Professor of Statistics at Columbia University in New York City. He received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 120 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He recently completed a term as Editor-in-Chief of Statistical Science and is the current editor of Statistical Analysis and Data Mining.

Shahn, Z., Ryan, P., and Madigan, D. (2015). Predicting Health Outcomes from High Dimensional Longitudinal Health Histories Using Relational Random Forests. Statistical Analysis and Data Mining, to appear.

Berger, M.L., Lipset, C., Gutteridge, A., Axelsen, K., Subedi, P., and Madigan, D. (2015). Optimizing the Leveraging of Real World Data: How It Can Improve the Development and Use of Medicines? Value in Health, http://dx.doi.org/10.1016/j.jval.2014.10.009.

Schuemie, M.J., Trifirò, G., Coloma, P.M., Ryan, P.B. and Madigan, D. (2014). Detecting adverse drug reactions following long-term exposure in longitudinal observational data. Statistical Methods in Medical Research, to appear.

Simpson, S., Madigan, D., Zorych, I., Schuemie, M.J., Ryan, P.B., and Suchard, M. (2013). Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics, DOI: 10.1111/biom.12078.

Ryan, P.B., Schuemie, M.J., Gruber, S., Zorych, I., and Madigan, D. (2013). Empirical Performance of a New User Cohort Method: Lessons for Developing a Risk Identification and Analysis System. Drug Safety, 36 (Suppl 1):S59-S72.

Madigan, D., Schuemie, M.J., and Ryan, P.B. (2013). Empirical Performance of the Case-Control Method: Lessons for Developing a Risk Identification and Analysis System. Drug Safety, 36 (Suppl 1):S73-S82.

Suchard, M.A., Zorych, I., Simpson, S.E., Schuemie, M.J., Ryan, P.B., and Madigan, D. (2013). Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System. Drug Safety, 36 (Suppl 1):S83-S93.

Ryan, P.B., Schuemie, M.J., and Madigan, D. (2013). Empirical Performance of a Self-Controlled Cohort Method: Lessons for Developing a Risk Identification and Analysis System. Drug Safety, 36 (Suppl 1):S95-S106.

Schuemie, M.J., Madigan, D., and Ryan, P.B. (2013). Empirical Performance of LGPS and LEOPARD: Lessons for Developing a Risk Identification and Analysis System. Drug Safety, 36 (Suppl 1):S133-S142.

Noren, G.N., Bergvall, T., Ryan, P.B., Juhlin, K., Schuemie, M.J., and Madigan, D. (2013). Empirical performance of the calibrated self-controlled cohort analysis within Temporal Pattern Discovery: Lessons for developing a risk identification and analysis system. Drug Safety, 36 (Suppl 1):Sxx-Sxx.

Ryan, P.B., Stang, P.E., Overhage, J.M., Suchard, M.A., Hartzema, A.G., DuMouchel, W., Reich, C.G., Schuemie, M.J., and Madigan, D. (2013). A Comparison of the Empirical Performance of Methods for a Risk Identification System. Drug Safety, 36 (Suppl 1):S143-S158.

DuMouchel, W., Ryan, P.B., Schuemie, M.J., and Madigan, D. (2013). Evaluation of disproportionality safety signalling applied to healthcare databases. Drug Safety, 36 (Suppl 1):S123-S132.

Hartzema, A.G., Reich, C.G., Ryan, P.B., Stang, P.E., Madigan, D., Welebob, E., Overhage, J.M. (2013). Managing data quality for a drug safety surveillance system. Drug Safety, 36 (Suppl 1):S49-S58.

Ryan, P.B., Madigan, D., Stang, P.E., Schuemie, M.J., and Hripcsak, G. (2013). Medication-wide association studies. CPT: Pharmacometrics & Systems Pharmacology 2, e76; doi:10.1038/psp.2013.52.

Madigan, D., Stang, P.E., Berlin, J.A., Schuemie, M.J., Overhage, J.M., Suchard, M.A., DuMouchel, W., Hartzema, A.G., and Ryan P.B. (2013). A Systematic Statistical Approach to Integrating Information from Observational Studies. Annual Review of Statistics and Its Application, 1, 11-39.

Schuemie, M., Ryan, P., DuMouchel, W., Suchard, M.A., and Madigan, D. (2013). Interpreting observational studies – why empirical calibration is needed to correct p-values. Statistics in Medicine, DOI: 10.1002/sim.5925.

Simpson, S., Madigan, D., Zorych, I., Schuemie, M.J., Ryan, P.B., and Suchard, M. (2013). Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics, 69, 893-902.

Ryan, P., Suchard, M.A., Schuemie, M., and Madigan, D. (2013). Learning from epidemiology: Interpreting observational studies for the effects of medical products. Statistics in Biopharmaceutical Research, DOI:10.1080/19466315.2013.791638.

Madigan, D., Ryan, P., Schuemie, M., Stang, P., Overhage, M., Hartzema, A., Suchard, M.A., DuMouchel, W., and Berlin, J. (2013). Evaluating the impact of database heterogeneity on observational studies. American Journal of Epidemiology, DOI: 10.1093/aje/kwt010..

Madigan, D., Ryan, P.B., and Schuemie, M.J. (2012). Does design matter? Systematic evaluation of the impact of analytical choices on effect estimates in observational studies. Therapeutic Advances in Drug Safety, 4, 53-62.

Ryan, P.B., Madigan, D., Stang, P.E., Overhage, J.M., Racoosin, J.A., Hartzema, A.G. (2012). Empirical Assessment of Analytic Methods for Risk Identification in Observational Healthcare Data: Results from the Experiments of the Observational Medical Outcomes Partnership. Statistics in Medicine, 30, 4401-4415.

Suchard, M., Simpson, S.E., Zorych, I., Ryan, P., and Madigan, D. (2013). Massive parallelization of serial inference algorithms for generalized linear models. ACM Transactions on Modeling and Computer Simulation, 23:1-17.

Harpaz, R., DuMouchel, W., Shah, N.H., Madigan, D., Ryan, P., and Friedman, C. (2012). Novel data mining methodologies for adverse drug event discovery and analysis. Clinical Pharmacology & Therapeutics, doi:10.1038/clpt.2012.50.

Zorych, I., Madigan, D., Ryan, P., and Bate, A. (2011). Disproportionality methods for pharmacovigilance in longitudinal observational databases. Statistical Methods in Medical Research, doi: 10.1177/0962280211403602.

Madigan, D. and Ryan, P. (2011). What can we really learn from observational studies? The need for empirical assessment of methodology for active drug safety surveillance and comparative effectiveness research. Epidemiology, 22 (5), 629-631.

Mittal, S., Madigan, D., Suchard, M., and Burd, R. (2013). High-Dimensional, Massive Sample-Size Cox Proportional Hazards Regression for Survival Analysis. Biostatistics, to appear.

Rudin, C., Letham, B., and Madigan, D. (2013). Learning theory analysis for association rules and sequential event prediction. Journal of Machine Learning Research, to appear.

Price, K. L., Xia, H.A., Lakshminarayanan, M., Madigan, D., Manner, D., Scott, J., Stamey, J., Thompson, L. (2014). Bayesian Methods for Design and Analysis of Safety Trials. Pharmaceutical Statistics, 13, 13-24.

Letham, B., Rudin, C., McCormick, T.H., and Madigan, D. (2013). An interpretable stroke prediction model with using rules using Bayesian analysis. Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13) Late Breaking Paper, to appear.

Letham, B., Rudin, C., and Madigan, D. (2013). A supervised ranking approach to sequential event prediction. Machine Learning, 10.1007/s10994-013-5356-5.

Mittal, S., Madigan, D., Cheng, J., and Burd, R. (2013). Large-scale Bayesian parametric survival analysis. Statistics in Medicine, to appear, DOI: 10.1002/sim.5817.

Madigan, D., Sigelman, D., Mayer, J.W., Furberg, C.D., Avorn, J. (2012). Under-reporting of cardiovascular events in the rofecoxib Alzheimer studies. American Heart Journal, doi:10.1016/j.ahj.2012.05.002.

Maclure, M., Fireman, B., Nelson, J.C., Hua, W., Shoaibi, A., Paredes, A., and Madigan, D. (2012). When should a distributed system for active medical product surveillance use case-based designs for safety monitoring?. Pharmacoepidemiology and Drug Safety, 21, 50-61.

McCormick, T., Rudin, C., and Madigan, D. (2012). A hierarchical model for association rule mining of sequential events: an approach to automated medical symptom prediction. Annals of Applied Statistics, 6, 652-658.

Rudin, C., Salleb-Aouissi, A., Kogan, E. and Madigan, D. (2011). Sequential Event Prediction with Association Rules. Proceedings of the 2011 Conference on Learning Theory (COLT) (30%). Also JMLR: Workshop and Conference Proceedings 19 (2011) 615-634.

Caster, O., Noren, G.N., Madigan, D., and Bate, A. (2010). Large-Scale Regression-Based Pattern Discovery: The Example of Screening the WHO Global Drug Safety Database. Statistical Analysis and Data Mining, 3, 197-208.