Zorych, I., Madigan, D., Ryan, P., and Bate, A. (2011). Disproportionality methods for pharmacovigilance in longitudinal observational databases. Submitted.

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. Submitted.

Madigan, D., Simpson, S., Hua, W., Paredes, A., Fireman, B., and Maclure, M. (2011). The self-controlled case series: Recent developments. Submitted.

Madigan, D., Hauben, M., Vallarino, C., Patadia, V., Gerrits, C., and Simpson, S. (2011). The self-controlled case series method applied to a claims database for detection of signals in pharmacovigilance: A pilot study. Submitted.

Madigan, D., Ryan, P., Simpson, S.E., and Zorych, I. (2010). Bayesian methods in pharmacovigilance (with discussion). In: J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (eds), Bayesian Statistics 9, Oxford University Press, to appear.

McCormick, T., Rudin, C., and Madigan, D. (2010). A hierarchical model for association rule mining of sequential events: An approach to automated medical symptom prediction. Submitted.

Rudin, C., Salleb-Aouissi, A., Kogan, E. and Madigan, D. (2010). Ranking association rules for recommender systems. Submitted.

McCormick, T., Madigan, D., Raftery, A.E., and Burd, R.S. (2010). Dynamic model averaging for logistic regression. Under revision at Biometrics.

Balakrishnan, S. and Madigan, D. (2010). Priors on the variance in sparse Bayesian learning: the demi-Bayesian lasso. In: Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger by Ming-Hui Chen, Peter Muller, Dongchu Sun, and Keying Ye.

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.

Ross, J.S., Madigan, D., Konstam, M.A., Egilman, D.S., and Krumholz, H.M. (2010). Does Rofecoxib cardiovascular risk persist after discontinuation? Archives of Internal Medicine, 170, 2035-2036.

Ross, J.S., Madigan, D., Hill, K.P., Egilman, D.S., Wang, Y., Krumholz, H.M. (2009). Pooled analysis of Rofecoxib placebo-controlled clinical trial data: Lessons for post-market pharmaceutical safety surveillance. Archives of Internal Medicine, 169, 1976-1985.

Burd, R. and Madigan, D. (2009). An evaluation of the impact of injury coding schemes on mortality prediction in pediatric trauma. Academic Emergency Medicine, 16, 639-645.

Pearson, R.K., Hauben, M., Goldsmith, D., Gould, A.L., Madigan, D., OHara, D.J., Reisinger, S., and Hochberg, A. (2009). Influence of the MEDDRA hierarchy on pharmacovigilance data mining results. International Journal of Medical Informatics, to appear.

Hochberg, A., Hauben, M., Pearson, R.K., OHara, D., Reisinger, S., Goldsmith, D.I., Gould, A.L., and Madigan, D. (2009). An Evaluation of Three Signal Detection Algorithms Using a Highly Inclusive Reference Event Database. Drug Safety, 32, 509-525.

Madigan, D. and Gelman, A. (2009). Discussion of "What is statistics," by Emery Brown and Robert Kass, American Statistician, 63, 114.

Madigan, D., Mittal, S., and Roberts, F. (2008). Efficient sequential decision making algorithms for for container inspection operations. Under revision at Naval Research Logistics.

Burd, R.S., Ouyang, M., and Madigan, D. (2008). Bayesian logistic injury severity score (BLISS): A method for predicting mortality using ICD-9 codes. Academic Emergency Medicine, 15(5), 466-475.

Caster, O., Noren, G.N., Madigan, D., and Bate, A. (2008). Large-scale regression-based pattern discovery: The example of the WHO drug safety database. KDD Workshop on Mining Medical Data, to appear.

Naik, P., Wedel, M., Bacon, L., Bodapati, A., Bradlow, E., Kamakura, W., Kreulen, J., Lenk, P., Madigan, D., and Montgomery, A. (2008). Challenges and Opportunities in High-Dimensional Choice Data Analysis. Marketing Letters.

Balakrishnan, S. and Madigan, D. (2007). Algorithms for Sparse Linear Classifiers in the Massive Data Setting. Journal of Machine Learning Research, 9, 313-337, 2007.

Balakrishnan, S. and Madigan, D. (2007). LAPS: Lasso with Partition Search. Proceedings of the IEEE International Conference on Data Mining, 415-420.

Hauben, M., Madigan, D., Reisinger, S., Hochberg, A., and OHara, D. (2007). Data Mining in Pharmacovigilence: Computational Cost as a Neglected Performance Parameter. International Journal of Pharmaceutical Medicine, 21, 319-323.

Hauben, M., Reich, L., Gerrits, C.M., and Madigan, D. (2007). Spontaneous Reporting of Hyperkalemia and The Randomized Aldactone Evaluation Study. Drug Safety, 30, 1143-1149.

Eyheramendy, D. and Madigan, D. (2007). A Bayesian feature selection score based on Naive Bayes models. In: Computational Methods of Feature Selection, H. Liu and H. Motoda, Editors, 277-294.

Madigan, D., Mittal, S., and Roberts, F. (2007). Sequential decision making algorithms for port of entry inspection: overcoming computational challenges. Proceedings of 2007 Intelligence and Security Informatics Conference, 1-7.

Rolka, H., Burkom, H., Cooper, G.F., Kulldorff, M., Madigan, D., and Wong, W-K. (2006). Issues in Applied Statistics for Public Health Bioterrorism Surveillance using Multiple Data Streams: Research needs. Statistics in Medicine, 26, 1834-1856.

Eyheramendy, S. and Madigan, D. (2007). A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization. In: IMS Lecture Notes - Monograph Series, Volume 54, Complex datasets and inverse problems: tomography, networks and beyond. Regina Liu, William Strawderman & Cun-Hui Zhang, Editors, 76-91.

Balakrishnan, S. and Madigan, D. (2006). Decision Trees for Functional Variables. Proceedings of the IEEE International Conference on Data Mining, 798-802.

Genkin, A., Lewis, D.D., and Madigan, D. (2007). Large-scale Bayesian logistic regression for text categorization. Technometrics, 49, 291-304.

Dayanik, A., Lewis, D.D., Madigan, D., Menkov, V., and Genkin, A. (2006). Constructing Informative Prior Distributions from Domain Knowledge in Text Classification, Proceedings of the 29th Annual International ACM SIGIR conference, 493-500.

Anand, S., Madigan, D., Mammone, R., Pathak, S. and Roberts, F. (2006). Experimental Analysis of Sequential Decision Making Algorithms for Port of Entry Inspection Procedures. In S. Mehrotra, D. Zeng, H. Chen, B. Thuraisingham, and F-X Wang (eds.), Intelligence and Security Informatics, Proceedings of ISI-2006, Lecture Notes in Computer Science #3975, Springer-Verlag, New York, 2006.

Balakrishnan, S. and Madigan, D. (2006). A One-Pass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets. Bayesian Analysis. 1, 345-362.

Madigan, D., Ju, W., Krishnan, P., and Krishnakumar, A.S. (2006). Location estimation in wireless networks: A Bayesian approach. Statistica Sinica, 16, 495-522.

Madigan, D., Vardi, Y., and Weissman, I. (2006). Extreme value theory applied to document retrieval from large collections. Information Retrieval, 9, 273-294.

Hauben, M., Madigan, D., Gerrits, C., and Meyboom, R. (2005). The role of data mining in pharmacovigilance. Expert Opinion in Drug Safety., 4(5), 929-948.

Madigan, D., Genkin, A., Lewis, D.D., and Fradkin, D. (2005). Bayesian multinomial logistic regression for author identification. Proceedings of the 25th International Workshop on Bayesian inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 05), 509-516.

Madigan, D., Genkin, A., Argamon, S., Fradkin, D., and Ye, L. (2005). Author identification. Proceedings of CSNA/Interface 05.

Eyheramendy, S. and Madigan, D. (2005). A Novel Feature Selection Score for Text Categorization. International Workshop on Feature Selection for Data Mining, 1-8.

Madigan, D. (2005). Statistics and Data Mining. In: AMS-DIMACS Discrete Methods in Epidemiology, James Abello and Graham Cormode (Editors), 21-24.

Hanks, S. and Madigan, D. (2005). Probabilistic temporal reasoning. In: Handbook of Temporal Reasoning in Artificial Intelligence, M. Fisher, D. Gabbay, and L. Vila, Editors, Elsevier B.V., 315-342.

Madigan, D., Elnahrawy, E., Martin, R.P., Ju, W., Krishnan, P. and Krishnakumar, A.S. (2004). Bayesian Indoor Positioning Systems. Proceedings of IEEE Infocom, 1217-1227.

Madigan, D. (2005). Bayesian data mining for surveillance. In: Spatial and Syndromic Surveillance for Public Health (Andrew Lawson and Ken Kleinman, Editors), 203-221.

Madigan, D. (2004). Statistics and the war on spam. In: Statistics: A Guide to the Unknown, Deb Nolan (Editor), 135-147.

Madigan, D. and Stuetzle, W. (2004). Graduate statistics education (discussion of A Report on the Future of Statistics). Statistical Science, 19, 408-409.

Fradkin, D. and Madigan, D. (2003). Experiments with random projections for machine learning. In Proceedings of KDD-03, The Ninth International Conference on Knowledge Discovery and Data Mining, 517-522.

Ridgeway, G. and Madigan, D. (2003). A sequential Monte Carlo Method for Bayesian analysis of massive datasets. Journal of Knowledge Discovery and Data Mining, 7, 301-319.

Eyheramendy, S., Lewis, David D., and Madigan, David (2003). On the naive bayes model for text classification. In Proceedings of The Ninth International Workshop on Artificial Intelligence and Statistics, C.M. Bishop and B.J. Frey (Editors), 332-339.

Madigan, D., Vardi, Y., and Weissman, I. (2003). On retrieval properties of samples of large collections. In Proceedings of The Ninth International Workshop on Artificial Intelligence and Statistics, C.M. Bishop and B.J. Frey (Editors), 265-270.

Madigan, D. and Ridgeway, G. (2003). Bayesian data analysis for data mining.  In Handbook of Data Mining, N. Ye (Ed.), 103-132..

Cohen, A., Madigan, D., and Sackrowitz, H.B. (2003). Effective directed tests for models with ordered categorical data. Australian and New Zealand Journal of Statistics, 45, 285-200. 2003 Best Paper Award.

Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma in Philadelphia schools. Chest, 124 (4): 141S.

Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma and tobacco: A survey of 65 Philadelphia middle schools. Chest, 124 (4): 141S-142S.

Dunbar, P.J., Madigan, D., Grohskopf, L.A., Revere, D., Woodward, J., Minstrell, J., Frick, P.A., Simoni, J.M., and Hooton, T.M. (2003). A two-way messaging system to enhance antiretroviral adherence. Journal of The American Medical Informatics Association, 10, 11-15.

Ridgeway, G. and Madigan, D. (2002). Bayesian analysis of massive datasets via particle filters  In Proceedings of KDD-02, The Eighth International Conference on Knowledge Discovery and Data Mining, 5-13.

Madigan, D., Raghavan, N., DuMouchel, W., Nason, M., Posse, C., and Ridgeway, G. (2002). Likelihood-based data squashing: A modeling approach to instance construction. Journal of Data Mining and Knowledge Discovery, 6, 173-190.

Tanimoto, S., Carlson, A., Husted, J., Hunt, E.B., Larsson, J., Madigan, D., and Minstrell, J. (2002). Text Forum Features for Small Group Discussions with Facet-Based Pedagogy. Proceedings of CSCL2002, Computer Supported Cooperative Learning.

Hoeting, J., Raftery, A.E., and Madigan, D. (2002). Amethod for simultaneous variable and transformation selection in linear regression. Journal of Computational and Graphical Statistics, 11, 485-507.

Liu, R., Madigan, D., and Eyheramendy, S. (2002). Text classification for mining aviation inspection reports. In: Statistical Data Analysis based on the L1-norm and Related methods. Birkhauser Statistics for Industry and technology, Y. Dodge editor, 379-392.

da Silva, C.Q., Zeh, J., Madigan, D., Laake, J., Rugh, D., Baraff, L., Koski, W., and Miller, G. (2001). Capture-recapture estimation of bowhead whale population size estimation using photo-identification data. Journal of Cetacean Research and Management, 2, 45-61.

Levitz, M., Perlman, M.D., and Madigan, D. (2001). Separation and Completeness Properties for AMP Chain Graph Markov Models. Annals of Statistics, 29, 1751–1784.

Church, L., Oliver, L., Dobie, S., Madigan, D., and Ellsworth, A. (2001). Analgesia for colposcopy: A double-blind, randomized comparison of ibuprofen and benzocaine gel for colposcopic analgesia. Obstetrics and Gynecology,97, 5-10.

Andersson, S.A., Madigan, D., and Perlman, M.D. (2001). An alternative Markov property for chain graphs. Scandinavian Journal of Statistics, 28, 33-85.

Glusker, A.I., Dobie, S.A., Madigan, D., Rosenblatt, R.A., Larson, E.H. (2000). Differences in fertility patterns between urban and rural women in Washington state, 1983-1984 to 1993-1994. Women and Health, 31, 55-70.

Kanungo, T., Haralick, R. M., Baird, H. Stuetzle, W., and Madigan, D. (2000). A statistical, nonparametric methodology for document degradation models validation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1209-1223.

Dobie, S.A., Hart, G., Glusker, A., Rosenblatt, R., and Madigan, D. (2000). Reproductive health services in rural Washington state: Scope of practice and the potential of medical abortions. American Journal of Public Health., 90, 624-626.

Madigan, D. and Nason, M. (2000). Statistics perspectives on data and knowledge. Handbook of Knowledge Discovery and Data Mining, Oxford University Press.

Nason, M. and Madigan, D. (2000). Sampling. Handbook of Knowledge Discovery and Data Mining, Oxford University Press.

Madigan, D., Raghavan, N., DuMouchel, W., Nason, M., Posse, C., and Ridgeway, G. (2000). Instance construction via likelihood-based data squashing. Instance Selection and Construction, A Data Mining Perspective, H. Motoda and H. Liu (Eds.), Kluwer, 209-226.

Hoeting, J.A.,  Madigan, D., Raftery, A.E., and Volinsky, C.T. (1999). Bayesian model averaging - a tutorial. Statistical Science, 14, 382-401.

Dobie, S.A., Hart, G., Glusker, A., Madigan, D., Larsen, E.B., and Rosenblatt, R. (1999). Abortion services in rural Washington State, 1983-1984 to 1993-1994: availability and outcomes. Family Planning Perspectives, 31, 241-245.

Condliff, M.K., Lewis, D.D., Madigan, D., and Posse, C. (1999). Bayesian mixed-effects models for recommender systems. Proceedings of SIGIR-99 Workshop on Recommender Systems.

Madigan, D. (1999). Bayesian Graphical Models, Intention-to-Treat, and the Rubin Causal Model. In Proceedings of Uncertainty-99, The Seventh International Workshop on Artificial Intelligence and Statistics, 123-132.

Golinelli, D., Madigan, D., and Consonni, G. (1999). Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models. In Proceedings of Uncertainty-99, The Seventh International Workshop on Artificial Intelligence and Statistics, 203-208.

Ridgeway, G., Madigan, D., and Richardson, T. (1999). Boosting Methodology for Regression Problems. In Proceedings of Uncertainty-99, The Seventh International Workshop on Artificial Intelligence and Statistics, 152-161.

Ridgeway, G., Madigan, D., Richardson, T., and O'Kane, K. (1998). Interpretable Boosted Naive Bayes Classification  In Proceedings of KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining, 101-104.

Andersson, S.A., Madigan, D., Perlman, M.D., and Richardson, T. (1998). Graphical Markov Models in multivariate analysis. In Multivariate Analysis, Design of Experiments, and Survey Sampling,  Subir Ghosh (Ed.), Marcel Dekker Inc.

Draper, D. and Madigan, D. (1997). The scientific value of Bayesian statistical methods. IEEE Intelligent Systems and their Applications, 12, 18-21.

Madigan, D. and York, J. (1997). Bayesian methods for estimating the size of a closed population. Biometrika, 84, 19-31.

Glymour, C., Madigan, D., Pregibon, D., and Smyth, P. (1997). Statistical themes and lessons for data mining. Journal of Data Mining and Knowledge Discovery, 1, 11-28.

Volinsky, C.T., Madigan, D., Raftery, A.E., and Kronmal, R.A. (1997). Bayesian Model Averaging in Proportional Hazard Models: Predicting Strokes. Applied Statistics 46, 433-448.

Andersson, S.A., Madigan, D., Perlman, M.D., and Triggs, C.M. (1997). A graphical characterization of lattice conditional independence models. Annals of Mathematics and Artificial Intelligence, 21, 27-50.

Madigan, D., Mosurski, K., and Almond, R.G. (1997). Explanation in belief networks. Journal of Computational and Graphical Statistics, 6, 160-181.

Andersson, S.A., Madigan, D., and Perlman, M.D., (1997). A characterization of Markov equivalence classes for acyclic digraphs. Annals of Statistics, 25, 505-541.

Raftery, A.E., Madigan, D., and Hoeting, J. (1997). Accounting for model uncertainty in linear regression. Journal of the American Statistical Association, 92, 179-191.

Madigan, D., Keim, M, and Lewis, D.D. (1997). Bayesian information retrieval. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 303-310.

Andersson, S.A., Madigan, D., and Perlman, M.D. (1997). On the Markov equivalence of chain graphs, undirected graphs, and acyclic digraphs. Scandinavian Journal of Statistics, 24, 81-102.

Madigan, D., Raftery, A.E., Volinsky, C.T., and Hoeting, J.A. (1996). Bayesian model averaging. In: Integrating Mulitple Learned Models (IMLM-96), P. Chan, S. Stolfo, and D. Wolpert (Eds.), 77-83.

Raftery, A.E., Madigan, D., and Volinsky, C.T. (1996). Accounting for model uncertainty in survival analysis improves predictive performance. In: Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith A. F. M., (eds.), Bayesian Statistics V, Oxford University Press, 323-350.

Andersson, S.A., Madigan, D., and Perlman, M.D. (1996). An alternative Markov property for chain graphs. In Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence,  Eric Horvitz and Finn Jensen (Eds.), Morgan Kaufmann Publishers, Inc., San Mateo.

Schaffner, A., Madigan, D., Hunt, E., Graf, E., Minstrell, J., and Nason, M. (1996). Benchmark lessons and the world wide web: Tools for teaching statistics. In: D. Edelson and E. Domeshek (Eds.) Proceedings of ICLS 96. Association for the Advancement of Computing in Education, 480-484.

Madigan, D. and Almond, R.G. (1996). On test selection strategies for belief networks. In Learning from Data: Artificial Intelligence and Statistics V, D. D. Fisher and H. Lenz (Eds.), Springer Verlag, 89-98.

Glymour, C., Madigan, D., Pregibon, D., and Smyth, P., and (1996). Statistical inference and data mining. Communications of the ACM, 39, 35-41.

Madigan, D., Andersson, S.A., Perlman, M.D, and Volinsky, C.T. (1996). Bayesian model averaging and model selection for Markov equivalence classes of acyclic digraphs. Communications in Statistics - Theory and Methods, 25, 2493-2519.

Hoeting, J.A., Madigan, D., and Raftery, A.E. (1996). A Method for Simultaneous Variable Selection and Outlier Identification in Linear Regression, Journal of Computational Statistics and Data Analysis, 22, 251-270.

Madigan, D., Chapman, C.R., Gavrin, J., Villumsen, O., and Boose, J.H. (1995). Repertory hypergrids for large-scale hypermedia linking. International Journal of Human-Computer Studies, 43, 465-481.

Madigan, D., Gavrin, J., and Raftery, A.E. (1995). Eliciting prior information to enhance the predictive performance of Bayesian graphical models. Communications in Statistics - Theory and Methods, 24, 2271-2292.

Madigan, D. and York, J. (1995). Bayesian graphical models for discrete data.  International Statistical Review, 63, 215-232.

Andersson, S.A., Madigan, D., Perlman, M.D., and Triggs, C.M., (1995). On the relation between conditional independence models determined by finite distributive lattices and by directed acyclic graphs. Journal of Statistical Planning and Inference, 46, 25-46.

York, J., Madigan, D., Heuch, I., and Lie, R.T. (1995). Estimation of the proportion of congenital malformations using double sampling: Incorporating covariates and accounting for model uncertainty. Applied Statistics, 44, 227–242.

Madigan, D., and Chapman, C.R. (1995). Multimedia tools for cancer pain education. In: Medical Multimedia, C. Ghaoui and R. Rada (Eds.), Intellect, Oxford, 121–136.

Haynor, D. and Madigan, D. (1995). Bayesian approach to differential diagnosis with use of Monte Carlo technique. Radiology, 197, 425S.

Hanks, S., Madigan, D., and Gavrin, J. (1995). Temporal reasoning in probabilistic knowledge-based systems. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence,  Philippe Besnard and Steve Hanks (Eds.), Morgan Kaufmann Publishers, Inc., San Mateo, 111-119.

Madigan, D., Chapman, C.R., Gavrin, J., Villumsen, O., and Boose, J.H. (1994). Repertory Hypergrids: An Application to Clinical Practice Guidelines. Proceedings of  the ACM European Conference on Hypermedia Technology, 117–125.

Almond, R, Bradshaw, J.M., and Madigan, D. (1994). Reuse and sharing of graphical belief network components. In Selecting Models from Data: Artificial Intelligence and Statistics IV, P. Cheeseman and W. Oldford (Eds.), Springer Verlag, 113-122.

Madigan, D., York, J.C., Bradshaw, J.M., and Almond, R.G. (1994). Bayesian graphical models for predicting errors in databases. In Selecting Models from Data: Artificial Intelligence and Statistics IV, P. Cheeseman and W. Oldford (Eds.), Springer Verlag, 123-132.

Madigan, D., Raftery, A.E., York, J.C., Bradshaw, J.M., and Almond, R.G. (1994). Strategies for graphical model selection. In Selecting Models from Data: Artificial Intelligence and Statistics IV, P. Cheeseman and W. Oldford (Eds.), Springer Verlag, 91-100.

York, J.C. and Madigan, D. (1994). Markov chain Monte Carlo methods for hierarchical Bayesian expert systems. In Selecting Models from Data: Artificial Intelligence and Statistics IV, P. Cheeseman and W. Oldford (Eds.), Springer Verlag.

Madigan, D., Chapman, C.R., Gavrin, J., Villumsen, O., and Boose, J.H. (1994). Modularized Maintenance for Hyperlinking: An Application to Clinical Practice Guidelines. Proceedings of the AAAI-94 Workshop on Indexing and Reuse in Multimedia Systems, 126–140.

Kanungo, T., Haralick, R. M., Stuetzle, W., and Madigan, D. (1994). Document Degradation Models: Parameter Estimation and model validation. In Proc. of Int. Workshop on Machine Vision Applications, Kawasaki, Japan, 552-557.

Madigan, D. and Raftery, A.E. (1994). Model Selection and accounting for model uncertainty in graphical models using Occam's window. Journal of the American Statistical Association, 89, 1535–1546.

Madigan, D. (1993). A note on equivalence classes of directed acyclic independence graphs. Probability in the Engineering and Informational Sciences, 7: 409–412.

Bradshaw, J.M., Chapman, C.R., Sullivan, K.M., Almond, R.G., Madigan, D., Zarley, D., Gavrin, J., Nims, J., and Bush, N. (1993). KS-3000: Applying DDUCKS to bone-marrow transplant patient support. Proceedings of the Sixth Annual Florida AI Research Symposium (FLAIRS '93), Ft. Lauderdale, FL, April, 78–83.

Bradshaw, J.M., Madigan, D., Richards, T., and Boy, G.A. (1993). Emerging technology and concepts for computer-based training. Proceedings of the Sixth Annual Florida AI Research Symposium (FLAIRS '93), Ft. Lauderdale, FL, April, 89–95.

Bradshaw, J.M., Richards, T., Fairweather, P., Buchanan, C., Guay, R., Madigan, D.,and Boy, G.A. (1993).  New directions in computer-based training in aerospace.  Proceedings of the Fourth International Conference on Human Machine Interactions and Artificial Intelligence in Aerospace. Toulouse, France.

Bradshaw, J.M., Chapman, C.R., Sullivan, K.M., Boose, J.H., Almond, R.G., Madigan, D., Zarley, D., Gavrin, J., Nims, J. and Bush, N. (1993). KS-3000: An application of DDUCKS to bone-marrow transplant patient support. Proceedings of the Seventh European Knowledge Acquisition for Knowledge-Based Systems Workshop (EKAW-93). Toulouse and Caylus, France, 57-74.

E.A. Kiely, Madigan, D., P.C. Ryan and M.R. Butler (1991). Ultrasonic imaging for extracorporeal shockwave lithotripsy: Analysis of factors in successful treatment. British Journal of Urology, 66: 127–131.

Madigan, D. and Mosurski, K. (1991). An extension of the results of Asmussen and Edwards on collapsibility in contingency tables. Biometrika, 77: 315–319 (Correction).


Pickering, W.H., Madigan, D., McCarter, R.J., and Burd, R.S. (2009). Evaluating relative importance of injury groupings on in-hospital mortality.

Cheng, J. and Madigan, D. (2008). Bayesian Approaches to Aspects of the Vioxx Trials: Non-ignorable Dropout and Sequential Meta-Analysis. Submitted.

Hauben, M., Madigan, D., Reisinger, S., Hochberg, A., and O’Hara, D. (2008). Effects of Stratification on Three Pharmacovigilance Data Mining Algorithms. Pharmacoepidemiology and Drug Safety, Submitted.