Preview
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EXHIBIT 130
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NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023
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NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023
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NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023
Exhibit 1
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David Madigan
davidbennettmadigan@gmail.com
Tel: (862) 812-3690
Curriculum Vitae
1 November 2022
Education
Trinity College Dublin, Ph.D., Statistics, 1990. Dissertation “An investigation of weights of evidence
in the context of probabilistic expert systems.” K. R. Mosurski, Advisor.
Trinity College Dublin, B.A. (Mod.), Mathematics, 1984, First Class Honours.
Employment History
2020 - : Northeastern University
2020 - : Provost & Senior Vice-President for Academic Affairs
2020 - : Professor of Statistics
2007 - 2020 : Columbia University
2007 - 2020 : Professor of Statistics
2013 - 2018 : Executive Vice-President for Arts and Sciences
2013 - 2018 : Dean of the Faculty of Arts and Sciences
2007 - 2013 : Chair, Department of Statistics
2001 - 2007 : Rutgers University
2001 - 2007 : Professor of Statistics and Biostatistics
2005 - 2007 : Dean, Physical and Mathematical Sciences
2003 - 2004 : Director, Institute of Biostatistics
2000 - 2001 : Vice President, Data Mining, Soliloquy, Inc.
1999 - 2000 : Principal Technical Staff Member, AT&T Labs-Research
1990 - 1999 : University of Washington/ Fred Hutchinson Cancer Research Center
1995 - 1999 : Associate Professor of Statistics, UW
1992 - 1999 : Assistant/Associate Member, FHCRC
1990 - 1995 : Assistant Professor of Statistics, UW
1989 - 1990 : Information Technology Consultant, KPMG, Ireland
1986 - 1989 : Technology Manager, Peregrine Expert Systems Ltd., Ireland
1985 - 1986 : Expert System Consultant, SkillSoft, Ireland
1984 - 1985 : Actuarial Associate, Hibernian Life Assurance, Ireland
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Honors
2014: Elected Member of the International Statistical Institute
2012: Elected Fellow of the American Association for the Advancement of Science.
2009: Institute of Mathematical Statistics Medallion Lecturer.
2006: Elected Fellow of the Institute of Mathematical Statistics.
2005: 36th Most Cited Mathematician in the World, 1995-2005, ISI Thomson.
1999: Elected Fellow of the American Statistical Association.
1995: University of Washington Distinguished Teaching Award.
1984: Gold medal awarded by the board of Trinity College Dublin.
1980: Trinity College Dublin, Entrance Scholarship in Mathematics.
Refereed Publications
1. Selzman, C.H., Feller, E.D., Walker, J.C., Sheridan, B.C., Silvestry, S.C., Daly, R.C., Anyanwu,
A.C., Madigan, D., Liu, P-Y., Frazier, O.H., and Griffith, B.P. (2022). The Jarvik 2000 Left
Ventricular Assist Device: Results of the United States Bridge to Transplant Trial. ASAIO
Journal, to appear.
2. Schuemie, M.J., Chen, Y., Madigan, D., and Suchard M. (2021). Combining Cox Regressions
Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts.
Statistical Methods in Medical Research, https://doi.org/10.1177/09622802211060518.
3. Zagar, A., Kadziola, Z., Lipkovich, I., Madigan, D., and Faries, D. (2021). Evaluating Bias
Control Strategies in Observational Studies Using Frequentist Model Averaging. Journal of
Biopharmaceutical Statistics, DOI: 10.1080/10543406.2021.1998095.
4. 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., Ryan, P.B., and Hripcsak, G., (2021). Comparative first-line
effectiveness and safety of angiotensin converting enzyme inhibitors and angiotensin receptor
blockers: a multinational cohort study. Hypertension,
https://doi.org/10.1161/HYPERTENSIONAHA.120.16667.
5. Hripcsak, G., Schuemie, M.J., Madigan, D., Ryan, P.B., and Suchard, M. (2021). Drawing
reproducible conclusions from observational clinical data with OHDSI. Yearbook of Medical
Informatics, DOI: 10.1055/s-0041-1726481.
6. Park, S., You, S.C., Krumholz, H.M., Suchard, M.A., Schuemie, M., Hripcsak, G., Chen, R.,
Shea, S., Duke, J., Pratt, N., Reich, C., Madigan, D., Ryan, P., and Park, R.W. (2021).
Comprehensive comparative effectiveness and safety of first-line beta-blocker monotherapy in
hypertensive patients: a large-scale multi-center observational study. Hypertension, to appear.
7. Dwivedi, R., Tan, Y. S., Park, B., Wei, M., Horgan, K., Madigan, D., & Yu, B. (2020). Stable
discovery of interpretable subgroups via calibration in causal studies. International Statistical
Review, 88, S1, S135-S178 doi:10.1111/insr.12427.
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8. You, S.C., Rho, Y., Bikdeli, B., Kim, J., Siapos, A., Weaver, J., Londhe, A., Cho, J.,Park, J.,
Schuemie, M., Suchard, M.A., Madigan, D., Hripcsak, G., Gupta, A., Reich, C. G., Ryan, P.B.,
Park, R.W., and Krumholz, H.M. (2020). Association of ticagrelor versus clopidogrel with net
adverse clinical events in patients with acute coronary syndrome undergoing percutaneous
coronary intervention. JAMA. 2020;324(16):1640-1650. doi:10.1001/jama.2020.16167.
9. Kim, Y., Tian, Y., Yang, J., Huser, V., Jin, P., Lambert, C., Park, H., You, S.C., Park, R.W.,
Rijnbeek, P., Zandt, M., Reich, C., Vashisht, R., Wu, Y., Duke, J., Hripcsak, G., Madigan, D.,
Shah, N., Ryan, P., Schuemie, M., Suchard, M. (2020). Comparative safety and effectiveness of
alendronate versus raloxifene in women with osteoporosis. Scientific Reports, 10.1 (2020): 1-10.
10. Schuemie, M.J., Ryan, P.B., Pratt, N., You, S.C., Krumholz, H.M., Madigan, D., Hripcsak, G.
and Suchard, M.A. (2020). Principles of Large-Scale Evidence Generation and Evaluation
across a Network of Databases (LEGEND). Journal of the American Medical Informatics Association,
https://doi.org/10.1093/jamia/ocaa103
11. Schuemie, M.J., Ryan, P.B., Pratt, N., You, S.C., Krumholz, H.M., Madigan, D., Hripcsak, G.
and Suchard, M.A. (2020). Large-Scale Evidence Generation and Evaluation across a Network
of Databases (LEGEND): Assessing Validity Using Hypertension as a Case Study. Journal of the
American Medical Informatics Association, https://doi.org/10.1093/jamia/ocaa124.
12. Schuemie, M.J., Cepeda, M.S., Suchard, M.A., Yang, J., Tian, Y., Schuler, A., Ryan, P.B.,
Madigan, D., and Hripcsak, G. (2020). How Confident Are We About Observational Findings
in Healthcare: A Benchmark Study. Harvard Data Science Review, 2.1, DOI:
10.1162/99608f92.147cc28e.
13. Hripcsak, G., Suchard, M.A., Shea, S., Chen, R., Pratt, N., Madigan, D., Krumholz, H.M., Ryan,
P.B., and Schuemie, M.J. (2019). Real-World Evidence on the Effectiveness and Safety of
Chlorthalidone and Hydrochlorothiazide. JAMA Internal Medicine,
doi:10.1001/jamainternmed.2019.7454.
14. Suchard, M.A., Schuemie, M.J., Krumholz, H.M., You, S., Chen, R., Pratt, N., Reich, C.G.,
Duke, J., Madigan, D., Hripcsak, G., and Ryan, P.B. (2019). Comprehensive comparative
effectiveness and safety of first-line antihypertensive drug classes. The Lancet,
DOI:https://doi.org/10.1016/S0140-6736(19)32317-7. International Medical Informatics Association
Best Paper 2019 - Bioinformatics and Translational Informatics.
15. Lu, F., Zheng, Y., Cleveland, H., Burton, C., and Madigan, D. (2018). Bayesian hierarchical
vector autoregressive models for patient-level predictive modeling. PLoS ONE,
https://doi.org/10.1371/journal.pone.0208082.
16. Schuemie, M., Ryan, P., Hripcsak, G., Madigan, D., and Suchard, M. (2018). Improving
reproducibility by using high-throughput observational studies with empirical calibration.
Philosophical Transactions A, 376:20170356. http://dx.doi.org/10.1098/rsta.2017.0356.
17. Madigan, D. and Shin, J. (2018). Drospirenone-Containing Oral Conraceptives and Venous
Thromboembolism: An Analysis of the FAERS Database. Open Access Journal of Contraception,
9:29-32.
18. Schuemie, M.J., Ryan, P., Hripcsak, G., Madigan, D., and Suchard, M. (2018). Empirical
confidence interval calibration for population-level effect estimation studies in observational
healthcare data. Proceedings of the National Academy of Science,
https://doi.org/10.1073/pnas.1708282114.
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19. Berger, M., Sox, H., Willke, R., Brixner, D., Eichler, H-G., Madigan, D., Makady, A.,
Schneeweiss, S., Tarricone, R., Wang, S., Mullins, D., Watkins, J. (2017). Recommendations for
Good Procedural Practices for Real-World Data Studies of Treatment Effectiveness and/or
Comparative Effectiveness: Report of the Joint ISPOR-ISPE Special Task Force on Real-World
Evidence in Health Care Decision Making. Pharmacoepidemiology and Drug Safety, DOI:
10.1002/pds.4297.
20. Zannad, F., Michael S. Lauer, Robert Temple, Marc A. Pfeffer, Deepak L. Bhatt, Denise E.
Bonds, Jeffrey S. Borer, Gonzalo Calvo, Louis Fiore, Lars H. Lund, David Madigan, Aldo P.
Maggioni, Jerry A. Menikoff, Catherine M. Meyers, Yves Rosenberg, Tabassome Simon, Wendy
Gattis Stough, Andrew Zalewski, Nevine Zariffa, Robert M. Califf (2017). Streamlining
Cardiovascular Clinical Trials to Achieve Improved Efficiency and Generalizability: Current
Progress and Future Steps. Heart, to appear.
21. Sobel, M., Madigan, D., and Wang, W. (2017). Meta-analysis: A causal framework, with
application to randomized studies of Vioxx. Psychometrika. 82: 459, 10.1007/s11336-016-9507-z
22. Shahn, Z. and Madigan, D. (2016). Latent Class Mixture Models of Treatment Effect
Heterogeneity. Bayesian Analysis, DOI: 10.1214/16-BA1022.
23. Shaddox, T.R., Ryan, P.B., Schuemie, M.J., Madigan, D., and Suchard, M.A. (2016). Hierarchical
models for multiple rare outcomes using massive observational heathcare databases. Statistical
Analysis and Data Mining, 9(4), 260-268.
24. Selzman, C. H., Felker, E., Sheridan, B. C., Silvestry, S., Daly, R. C., Anyanwu, A., Madigan, D.,
Frazier, O. & Griffith, B. P. (2016). The Jarvik 2000: Results of the United States Bridge to
Transplant Trial. The Journal of Heart and Lung Transplantation, 35(4), S38.
25. Webman, R. B., Carter, E. A., Mittal, S., Wang, J., Sathya, C., Nathens, A. B., Nance, M.L.,
Madigan, D. & Burd, R. S. (2016). Association between trauma center type and mortality among
injured adolescent patients. JAMA Pediatrics, 170(8), 780-786.
26. Hripcsak, G., Patrick Ryan, Jon Duke, Nigam H. Shah, Rae Woong Park, Vojtech Huserh, Marc
A. Suchard, Martijn Schuemie, Frank DeFalco, Adler Perotte, Juan Banda, Christian Reich, Lisa
Schilling, Michael Matheny, Daniella Meeker, Nicole Pratt, and Madigan, D. (2016). Addressing
Clinical Questions at Scale: OHDSI Characterization of Treatment Pathways. Proceedings of the
National Academy of Sciences, vol. 113 no. 277329–7336, doi: 10.1073/pnas.1510502113.
27. Moghaddass, R., Rudin, C., and Madigan, D. (2016). The Factorized Self-Controlled Case Series
Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. Journal of
Machine Learning Research, 17(185):1−24, 2016.
28. Beck, H.E., Mittal, S., Madigan, D., and Burd, R.S. (2015). Reassessing mechanism as a
predictor of pediatric injury mortality. Journal of Surgical Research 199 (2), 641-646.
29. Boland, M.R., Z Shahn, D Madigan, G Hripcsak, NP Tatonetti (2015). Birth Month Affects
Lifetime Disease Risk: A Phenome-Wide Method. Journal of the American Medical Informatics
Association, DOI: http://dx.doi.org/10.1093/jamia/ocv046.
30. 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, 8:128-136, DOI: 10.1002/sam.11268.
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31. 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.
32. Hripcsak, G., Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J
Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, Johan van
der Lei, Nicole Pratt, G Niklas Norén, Yu-Chuan Li, Paul E Stang, David Madigan, and Patrick
B Ryan (2015). Observational Health Data Sciences and Informatics (OHDSI): Opportunities
for Observational Researchers. MedInfo, Stud Health Technol Inform. 2015; 216: 574–578.
33. Letham, B., Rudin, C., McCormick, T.H., and Madigan, D. (2014). Interpretable classifiers using
rules and Bayesian analysis: Building a better stroke prediction model. Annals of Applied Statistics,
9, 1350-1371, DOI: 10.1214/15-AOAS848.
34. Hripcsak, G., Varela, S.V., Ryan, P.B., Madigan, D., Stang, P., Schuemie, M., Friedman, C., and
Tatonetti, N. (2014). Similarity-based Modeling applied to Signal Detection in
Pharmacovigilance. CPT: Pharmacometrics & Systems Pharmacology, 3(9), 1-9.
35. 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, doi:10.1177/0962280214527531.
36. 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.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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):S107-S121.
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44. 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.
45. 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.
46. 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.
47. 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.
48. Mittal, S., Madigan, D., Suchard, M., and Burd, R. (2013). High-Dimensional, Massive Sample-
Size Cox Proportional Hazards Regression for Survival Analysis. Biostatistics,
doi: 10.1093/biostatistics/kxt043.
49. Rudin, C., Letham, B., and Madigan, D. (2013). Learning theory analysis for association rules
and sequential event prediction. Journal of Machine Learning Research, 14, 3441-3492.
50. 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.
51. 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.
52. 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.
53. 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.
54. Letham, B., Rudin, C., and Madigan, D. (2013). A supervised ranking approach to sequential
event prediction. Machine Learning, 10.1007/s10994-013-5356-5.
55. 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.
56. Mittal, S., Madigan, D., Cheng, J., and Burd, R. (2013). Large-scale Bayesian parametric survival
analysis. Statistics in Medicine, DOI: 10.1002/sim.5817.
57. Emir, B., Amaratunga, D., Beltangady, M., Cabrera, J., Freeman, R., Madigan, D., Nguyen, H.,
and Whalen, E. (2013). Generating productive dialogue between consulting statisticians and
their clients ion the pharmaceutical and medical research settings. Open Access Medical Statistics, 3,
51-56.
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58. 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.
59. 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.
60. 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.
61. 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.
62. 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.
63. 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.
64. 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.
65. McCormick, T., Madigan, D., Raftery, A.E., and Burd, R.S. (2012). Dynamic model averaging
for logistic regression. Biometrics, 68:23-30.
66. 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.
67. Oquendo, M.A., Baca-Garcia, E., Artes, A., Perez-Cruz, F, Galfalvy H.C., Blasco-Fontecilla, H.,
Madigan, D., Duan, N. (2011). Hypothesis Generation in the 21st Century. Molecular Psychiatry,
to appear.
68. Smith, R.T., Merriam, J.E., Sohrab, M.A., Pumariega, N.M., Barile, G., Blonska, A.M., Haans, R,
Madigan, D., and Allikmets, R. (2011). Complement Factor H 402H Variant and Reticular
Macular Disease. Archives of Ophthalmolology 129(8):1061-1066.
69. Madigan, D., Mittal, S., and Roberts, F. (2011). Efficient sequential decision making algorithms
for container inspection operations. Naval Research Logistics, 58, 637-654.
70. 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.
71. 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.
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72. 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.
73. 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, 421-438.
74. 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 Müller, Dongchu Sun, and Keying Ye.
75. 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.
76. 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.
77. Cheng, J. and Madigan, D. (2010). Bayesian Approaches to Aspects of the Vioxx Trials: Non-
ignorable Dropout and Sequential Meta-Analysis. In: Handbook of Applied Bayesian Analysis,
Oxford University Press, 51-68.
78. 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.
79. 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.
80. Pearson, R.K., Hauben, M., Goldsmith, D., Gould, A.L., Madigan, D., O’Hara, D.J., Reisinger,
S., and Hochberg, A. (2009). Influence of the MEDDRA hierarchy on pharmacovigilance data
mining results. International Journal of Medical Informatics, 78, e97-e103.
81. Hochberg, A., Hauben, M., Pearson, R.K., O’Hara, 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.
82. 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.
83. 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.
84. 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, 19 (3), 201-213.
85. 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.
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86. Balakrishnan, S. and Madigan, D. (2007). LAPS: Lasso with Partition Search. Proceedings of the
IEEE International Conference on Data Mining, 415-420, 19% acceptance rate.
87. Hauben, M., Madigan, D., Reisinger, S., Hochberg, A., and O’Hara, D. (2007). Data Mining in
Pharmacovigilence: Computational Cost as a Neglected Performance Parameter. International
Journal of Pharmaceutical Medicine, 21, 319-323.
88. 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.
89. 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.
90. 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.
91. 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.
92. Genkin, A., Lewis, D.D., and Madigan, D. (2007). Large-scale Bayesian logistic regression for
text categorization. Technometrics, 49, 291-304.
93. 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.
94. Balakrishnan, S. and Madigan, D. (2006). Decision Trees for Functional Variables. Proceedings of
the IEEE International Conference on Data Mining, 798-802, 20% acceptance rate.
95. 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 (18.5% acceptance rate).
96. 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.
97. Balakrishnan, S. and Madigan, D. (2006). A One-Pass Sequential Monte Carlo Method for
Bayesian Analysis of Massive Datasets. Bayesian Analysis. 1, 345-362.
98. Madigan, D., Ju, W., Krishnan, P., and Krishnakumar, A.S. (2006). Location estimation in
wireless networks: A Bayesian approach. Statistica Sinica, 16, 495-522.
99. Madigan, D., Vardi, Y., and Weissman, I. (2006). Extreme value theory applied to document
retrieval from large collections. Information Retrieval, 9, 273-294.
100. Hauben, M., Madigan, D., Gerrits, C.M., Walsh, L., and Van Puijenbroek, E.P. (2005). The role
of data mining in pharmacovigilance. Expert Opinion in Drug Safety., 4(5), 929-948.
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101. 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.
102. Madigan, D., Genkin, A., Argamon, S., Fradkin, D., and Ye, L. (2005). Author identification.
Proceedings of CSNA/Interface 05.
103. Eyheramendy, S. and Madigan, D. (2005). A Novel Feature Selection Score for Text
Categorization. International Workshop on Feature Selection for Data Mining, 1-8, 24% acceptance
rate.
104. Madigan, D. (2005). Statistics and Data Mining. In: AMS-DIMACS Discrete Methods in
Epidemiology, James Abello and Graham Cormode (Editors), 21-24.
105. 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.
106. Madigan, D. (2005). Bayesian data mining for surveillance. In: Spatial and Syndromic Surveillance for
Public Health (Andrew Lawson and Ken Kleinman, Editors), 203-221.
107. 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 (17% acceptance
rate)
108. Madigan, D. (2004). Statistics and the war on spam. In: Statistics: A Guide to the Unknown, Deb
Nolan (Editor), 135-147.
109. 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.
110. 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.
111. Eyheramendy, S., Lewis, David D., and Madigan, David (2003). On the naïve 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.
112. 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.
113. Madigan, D. and Ridgeway, G. (2003). Bayesian data analysis for data mining. In Handbook of
Data Mining, N. Ye (Ed.), 103-132..
114. 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.
115. Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma in Philadelphia schools. Chest, 124 (4):
141S.
116. Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma and tobacco: A survey of 65
Philadelphia middle schools. Chest, 124 (4): 141S-142S.
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117. 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.
118. 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.
119. 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.
120. 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.
121. Hoeting, J., Raftery, A.E., and Madigan, D. (2002). A method for simultaneous variable and
transformation selection in linear regression. Journal of Computational and Graphical Statistics, 11,
485-507.
122. 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.
123. 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.
124. 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.
125. 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.
126. Andersson, S.A., Madigan, D., and Perlman, M.D. (2001). An alternative Markov property for
chain graphs. Scandinavian Journal of Statistics, 28, 33-85.
127. 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.
128. 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.
129. 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.
130. Madigan, D. and Nason, M. (2000). Statistics perspectives on data and knowledge. Handbook of
Knowledge Discovery and Data Mining, Oxford University Press.
131. Nason, M. and Madigan, D. (2000). Sampling. Handbook of Knowledge Discovery and Data Mining,
Oxford University Press.
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132. 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.
133. Hoeting, J.A., Madigan, D., Raftery, A.E., and Volinsky, C.T. (1999). Bayesian model averaging
– a tutorial. Statistical Science, 14, 382-401.
134. 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.
135. 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.
136. 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.
137. 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.
138. 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.
139. Ridgeway, G., Madigan, D., Richardson, T., and O’Kane, K. (1998). Interpretable Boosted
Naïve Bayes Classification In Proceedings of KDD-98, The Fourth International Conference on Knowledge
Discovery and Data Mining, 101-104.
140. 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.
141. Draper, D. and Madigan, D. (1997). The scientific value of Bayesian statistical methods. IEEE
Intelligent Systems and their Applications, 12, 18-21.
142. Madigan, D. and York, J. (1997). Bayesian methods for estimating the size of a closed
population. Biometrika, 84, 19-31.
143. 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.
144. 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.
145. 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.
146. Madigan, D., Mosurski, K., and Almond, R.G. (1997). Explanation in belief networks. Journal of
Computational and Graphical Statistics, 6, 160-181.
147. 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.
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148. 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.
149. 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.
150. 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.
151. 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.
152. Raftery, A.E., Madigan, D., and Volinsky, C.T. (1996). Accounting for mo
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FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 EXHIBIT 130 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 Exhibit 1 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan davidbennettmadigan@gmail.com Tel: (862) 812-3690 Curriculum Vitae 1 November 2022 Education Trinity College Dublin, Ph.D., Statistics, 1990. Dissertation “An investigation of weights of evidence in the context of probabilistic expert systems.” K. R. Mosurski, Advisor. Trinity College Dublin, B.A. (Mod.), Mathematics, 1984, First Class Honours. Employment History 2020 - : Northeastern University 2020 - : Provost & Senior Vice-President for Academic Affairs 2020 - : Professor of Statistics 2007 - 2020 : Columbia University 2007 - 2020 : Professor of Statistics 2013 - 2018 : Executive Vice-President for Arts and Sciences 2013 - 2018 : Dean of the Faculty of Arts and Sciences 2007 - 2013 : Chair, Department of Statistics 2001 - 2007 : Rutgers University 2001 - 2007 : Professor of Statistics and Biostatistics 2005 - 2007 : Dean, Physical and Mathematical Sciences 2003 - 2004 : Director, Institute of Biostatistics 2000 - 2001 : Vice President, Data Mining, Soliloquy, Inc. 1999 - 2000 : Principal Technical Staff Member, AT&T Labs-Research 1990 - 1999 : University of Washington/ Fred Hutchinson Cancer Research Center 1995 - 1999 : Associate Professor of Statistics, UW 1992 - 1999 : Assistant/Associate Member, FHCRC 1990 - 1995 : Assistant Professor of Statistics, UW 1989 - 1990 : Information Technology Consultant, KPMG, Ireland 1986 - 1989 : Technology Manager, Peregrine Expert Systems Ltd., Ireland 1985 - 1986 : Expert System Consultant, SkillSoft, Ireland 1984 - 1985 : Actuarial Associate, Hibernian Life Assurance, Ireland FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 2 Honors 2014: Elected Member of the International Statistical Institute 2012: Elected Fellow of the American Association for the Advancement of Science. 2009: Institute of Mathematical Statistics Medallion Lecturer. 2006: Elected Fellow of the Institute of Mathematical Statistics. 2005: 36th Most Cited Mathematician in the World, 1995-2005, ISI Thomson. 1999: Elected Fellow of the American Statistical Association. 1995: University of Washington Distinguished Teaching Award. 1984: Gold medal awarded by the board of Trinity College Dublin. 1980: Trinity College Dublin, Entrance Scholarship in Mathematics. Refereed Publications 1. Selzman, C.H., Feller, E.D., Walker, J.C., Sheridan, B.C., Silvestry, S.C., Daly, R.C., Anyanwu, A.C., Madigan, D., Liu, P-Y., Frazier, O.H., and Griffith, B.P. (2022). The Jarvik 2000 Left Ventricular Assist Device: Results of the United States Bridge to Transplant Trial. ASAIO Journal, to appear. 2. Schuemie, M.J., Chen, Y., Madigan, D., and Suchard M. (2021). Combining Cox Regressions Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts. Statistical Methods in Medical Research, https://doi.org/10.1177/09622802211060518. 3. Zagar, A., Kadziola, Z., Lipkovich, I., Madigan, D., and Faries, D. (2021). Evaluating Bias Control Strategies in Observational Studies Using Frequentist Model Averaging. Journal of Biopharmaceutical Statistics, DOI: 10.1080/10543406.2021.1998095. 4. 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., Ryan, P.B., and Hripcsak, G., (2021). Comparative first-line effectiveness and safety of angiotensin converting enzyme inhibitors and angiotensin receptor blockers: a multinational cohort study. Hypertension, https://doi.org/10.1161/HYPERTENSIONAHA.120.16667. 5. Hripcsak, G., Schuemie, M.J., Madigan, D., Ryan, P.B., and Suchard, M. (2021). Drawing reproducible conclusions from observational clinical data with OHDSI. Yearbook of Medical Informatics, DOI: 10.1055/s-0041-1726481. 6. Park, S., You, S.C., Krumholz, H.M., Suchard, M.A., Schuemie, M., Hripcsak, G., Chen, R., Shea, S., Duke, J., Pratt, N., Reich, C., Madigan, D., Ryan, P., and Park, R.W. (2021). Comprehensive comparative effectiveness and safety of first-line beta-blocker monotherapy in hypertensive patients: a large-scale multi-center observational study. Hypertension, to appear. 7. Dwivedi, R., Tan, Y. S., Park, B., Wei, M., Horgan, K., Madigan, D., & Yu, B. (2020). Stable discovery of interpretable subgroups via calibration in causal studies. International Statistical Review, 88, S1, S135-S178 doi:10.1111/insr.12427. 2 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 3 8. You, S.C., Rho, Y., Bikdeli, B., Kim, J., Siapos, A., Weaver, J., Londhe, A., Cho, J.,Park, J., Schuemie, M., Suchard, M.A., Madigan, D., Hripcsak, G., Gupta, A., Reich, C. G., Ryan, P.B., Park, R.W., and Krumholz, H.M. (2020). Association of ticagrelor versus clopidogrel with net adverse clinical events in patients with acute coronary syndrome undergoing percutaneous coronary intervention. JAMA. 2020;324(16):1640-1650. doi:10.1001/jama.2020.16167. 9. Kim, Y., Tian, Y., Yang, J., Huser, V., Jin, P., Lambert, C., Park, H., You, S.C., Park, R.W., Rijnbeek, P., Zandt, M., Reich, C., Vashisht, R., Wu, Y., Duke, J., Hripcsak, G., Madigan, D., Shah, N., Ryan, P., Schuemie, M., Suchard, M. (2020). Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis. Scientific Reports, 10.1 (2020): 1-10. 10. Schuemie, M.J., Ryan, P.B., Pratt, N., You, S.C., Krumholz, H.M., Madigan, D., Hripcsak, G. and Suchard, M.A. (2020). Principles of Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND). Journal of the American Medical Informatics Association, https://doi.org/10.1093/jamia/ocaa103 11. Schuemie, M.J., Ryan, P.B., Pratt, N., You, S.C., Krumholz, H.M., Madigan, D., Hripcsak, G. and Suchard, M.A. (2020). Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND): Assessing Validity Using Hypertension as a Case Study. Journal of the American Medical Informatics Association, https://doi.org/10.1093/jamia/ocaa124. 12. Schuemie, M.J., Cepeda, M.S., Suchard, M.A., Yang, J., Tian, Y., Schuler, A., Ryan, P.B., Madigan, D., and Hripcsak, G. (2020). How Confident Are We About Observational Findings in Healthcare: A Benchmark Study. Harvard Data Science Review, 2.1, DOI: 10.1162/99608f92.147cc28e. 13. Hripcsak, G., Suchard, M.A., Shea, S., Chen, R., Pratt, N., Madigan, D., Krumholz, H.M., Ryan, P.B., and Schuemie, M.J. (2019). Real-World Evidence on the Effectiveness and Safety of Chlorthalidone and Hydrochlorothiazide. JAMA Internal Medicine, doi:10.1001/jamainternmed.2019.7454. 14. Suchard, M.A., Schuemie, M.J., Krumholz, H.M., You, S., Chen, R., Pratt, N., Reich, C.G., Duke, J., Madigan, D., Hripcsak, G., and Ryan, P.B. (2019). Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes. The Lancet, DOI:https://doi.org/10.1016/S0140-6736(19)32317-7. International Medical Informatics Association Best Paper 2019 - Bioinformatics and Translational Informatics. 15. Lu, F., Zheng, Y., Cleveland, H., Burton, C., and Madigan, D. (2018). Bayesian hierarchical vector autoregressive models for patient-level predictive modeling. PLoS ONE, https://doi.org/10.1371/journal.pone.0208082. 16. Schuemie, M., Ryan, P., Hripcsak, G., Madigan, D., and Suchard, M. (2018). Improving reproducibility by using high-throughput observational studies with empirical calibration. Philosophical Transactions A, 376:20170356. http://dx.doi.org/10.1098/rsta.2017.0356. 17. Madigan, D. and Shin, J. (2018). Drospirenone-Containing Oral Conraceptives and Venous Thromboembolism: An Analysis of the FAERS Database. Open Access Journal of Contraception, 9:29-32. 18. Schuemie, M.J., Ryan, P., Hripcsak, G., Madigan, D., and Suchard, M. (2018). Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proceedings of the National Academy of Science, https://doi.org/10.1073/pnas.1708282114. 3 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 4 19. Berger, M., Sox, H., Willke, R., Brixner, D., Eichler, H-G., Madigan, D., Makady, A., Schneeweiss, S., Tarricone, R., Wang, S., Mullins, D., Watkins, J. (2017). Recommendations for Good Procedural Practices for Real-World Data Studies of Treatment Effectiveness and/or Comparative Effectiveness: Report of the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making. Pharmacoepidemiology and Drug Safety, DOI: 10.1002/pds.4297. 20. Zannad, F., Michael S. Lauer, Robert Temple, Marc A. Pfeffer, Deepak L. Bhatt, Denise E. Bonds, Jeffrey S. Borer, Gonzalo Calvo, Louis Fiore, Lars H. Lund, David Madigan, Aldo P. Maggioni, Jerry A. Menikoff, Catherine M. Meyers, Yves Rosenberg, Tabassome Simon, Wendy Gattis Stough, Andrew Zalewski, Nevine Zariffa, Robert M. Califf (2017). Streamlining Cardiovascular Clinical Trials to Achieve Improved Efficiency and Generalizability: Current Progress and Future Steps. Heart, to appear. 21. Sobel, M., Madigan, D., and Wang, W. (2017). Meta-analysis: A causal framework, with application to randomized studies of Vioxx. Psychometrika. 82: 459, 10.1007/s11336-016-9507-z 22. Shahn, Z. and Madigan, D. (2016). Latent Class Mixture Models of Treatment Effect Heterogeneity. Bayesian Analysis, DOI: 10.1214/16-BA1022. 23. Shaddox, T.R., Ryan, P.B., Schuemie, M.J., Madigan, D., and Suchard, M.A. (2016). Hierarchical models for multiple rare outcomes using massive observational heathcare databases. Statistical Analysis and Data Mining, 9(4), 260-268. 24. Selzman, C. H., Felker, E., Sheridan, B. C., Silvestry, S., Daly, R. C., Anyanwu, A., Madigan, D., Frazier, O. & Griffith, B. P. (2016). The Jarvik 2000: Results of the United States Bridge to Transplant Trial. The Journal of Heart and Lung Transplantation, 35(4), S38. 25. Webman, R. B., Carter, E. A., Mittal, S., Wang, J., Sathya, C., Nathens, A. B., Nance, M.L., Madigan, D. & Burd, R. S. (2016). Association between trauma center type and mortality among injured adolescent patients. JAMA Pediatrics, 170(8), 780-786. 26. Hripcsak, G., Patrick Ryan, Jon Duke, Nigam H. Shah, Rae Woong Park, Vojtech Huserh, Marc A. Suchard, Martijn Schuemie, Frank DeFalco, Adler Perotte, Juan Banda, Christian Reich, Lisa Schilling, Michael Matheny, Daniella Meeker, Nicole Pratt, and Madigan, D. (2016). Addressing Clinical Questions at Scale: OHDSI Characterization of Treatment Pathways. Proceedings of the National Academy of Sciences, vol. 113 no. 277329–7336, doi: 10.1073/pnas.1510502113. 27. Moghaddass, R., Rudin, C., and Madigan, D. (2016). The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. Journal of Machine Learning Research, 17(185):1−24, 2016. 28. Beck, H.E., Mittal, S., Madigan, D., and Burd, R.S. (2015). Reassessing mechanism as a predictor of pediatric injury mortality. Journal of Surgical Research 199 (2), 641-646. 29. Boland, M.R., Z Shahn, D Madigan, G Hripcsak, NP Tatonetti (2015). Birth Month Affects Lifetime Disease Risk: A Phenome-Wide Method. Journal of the American Medical Informatics Association, DOI: http://dx.doi.org/10.1093/jamia/ocv046. 30. 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, 8:128-136, DOI: 10.1002/sam.11268. 4 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 5 31. 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. 32. Hripcsak, G., Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, Johan van der Lei, Nicole Pratt, G Niklas Norén, Yu-Chuan Li, Paul E Stang, David Madigan, and Patrick B Ryan (2015). Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. MedInfo, Stud Health Technol Inform. 2015; 216: 574–578. 33. Letham, B., Rudin, C., McCormick, T.H., and Madigan, D. (2014). Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. Annals of Applied Statistics, 9, 1350-1371, DOI: 10.1214/15-AOAS848. 34. Hripcsak, G., Varela, S.V., Ryan, P.B., Madigan, D., Stang, P., Schuemie, M., Friedman, C., and Tatonetti, N. (2014). Similarity-based Modeling applied to Signal Detection in Pharmacovigilance. CPT: Pharmacometrics & Systems Pharmacology, 3(9), 1-9. 35. 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, doi:10.1177/0962280214527531. 36. 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. 37. 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. 38. 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. 39. 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. 40. 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. 41. 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. 42. 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. 43. 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):S107-S121. 5 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 6 44. 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. 45. 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. 46. 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. 47. 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. 48. Mittal, S., Madigan, D., Suchard, M., and Burd, R. (2013). High-Dimensional, Massive Sample- Size Cox Proportional Hazards Regression for Survival Analysis. Biostatistics, doi: 10.1093/biostatistics/kxt043. 49. Rudin, C., Letham, B., and Madigan, D. (2013). Learning theory analysis for association rules and sequential event prediction. Journal of Machine Learning Research, 14, 3441-3492. 50. 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. 51. 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. 52. 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. 53. 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. 54. Letham, B., Rudin, C., and Madigan, D. (2013). A supervised ranking approach to sequential event prediction. Machine Learning, 10.1007/s10994-013-5356-5. 55. 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. 56. Mittal, S., Madigan, D., Cheng, J., and Burd, R. (2013). Large-scale Bayesian parametric survival analysis. Statistics in Medicine, DOI: 10.1002/sim.5817. 57. Emir, B., Amaratunga, D., Beltangady, M., Cabrera, J., Freeman, R., Madigan, D., Nguyen, H., and Whalen, E. (2013). Generating productive dialogue between consulting statisticians and their clients ion the pharmaceutical and medical research settings. Open Access Medical Statistics, 3, 51-56. 6 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 7 58. 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. 59. 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. 60. 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. 61. 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. 62. 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. 63. 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. 64. 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. 65. McCormick, T., Madigan, D., Raftery, A.E., and Burd, R.S. (2012). Dynamic model averaging for logistic regression. Biometrics, 68:23-30. 66. 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. 67. Oquendo, M.A., Baca-Garcia, E., Artes, A., Perez-Cruz, F, Galfalvy H.C., Blasco-Fontecilla, H., Madigan, D., Duan, N. (2011). Hypothesis Generation in the 21st Century. Molecular Psychiatry, to appear. 68. Smith, R.T., Merriam, J.E., Sohrab, M.A., Pumariega, N.M., Barile, G., Blonska, A.M., Haans, R, Madigan, D., and Allikmets, R. (2011). Complement Factor H 402H Variant and Reticular Macular Disease. Archives of Ophthalmolology 129(8):1061-1066. 69. Madigan, D., Mittal, S., and Roberts, F. (2011). Efficient sequential decision making algorithms for container inspection operations. Naval Research Logistics, 58, 637-654. 70. 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. 71. 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. 7 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 8 72. 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. 73. 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, 421-438. 74. 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 Müller, Dongchu Sun, and Keying Ye. 75. 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. 76. 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. 77. Cheng, J. and Madigan, D. (2010). Bayesian Approaches to Aspects of the Vioxx Trials: Non- ignorable Dropout and Sequential Meta-Analysis. In: Handbook of Applied Bayesian Analysis, Oxford University Press, 51-68. 78. 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. 79. 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. 80. Pearson, R.K., Hauben, M., Goldsmith, D., Gould, A.L., Madigan, D., O’Hara, D.J., Reisinger, S., and Hochberg, A. (2009). Influence of the MEDDRA hierarchy on pharmacovigilance data mining results. International Journal of Medical Informatics, 78, e97-e103. 81. Hochberg, A., Hauben, M., Pearson, R.K., O’Hara, 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. 82. 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. 83. 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. 84. 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, 19 (3), 201-213. 85. 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. 8 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 9 86. Balakrishnan, S. and Madigan, D. (2007). LAPS: Lasso with Partition Search. Proceedings of the IEEE International Conference on Data Mining, 415-420, 19% acceptance rate. 87. Hauben, M., Madigan, D., Reisinger, S., Hochberg, A., and O’Hara, D. (2007). Data Mining in Pharmacovigilence: Computational Cost as a Neglected Performance Parameter. International Journal of Pharmaceutical Medicine, 21, 319-323. 88. 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. 89. 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. 90. 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. 91. 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. 92. Genkin, A., Lewis, D.D., and Madigan, D. (2007). Large-scale Bayesian logistic regression for text categorization. Technometrics, 49, 291-304. 93. 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. 94. Balakrishnan, S. and Madigan, D. (2006). Decision Trees for Functional Variables. Proceedings of the IEEE International Conference on Data Mining, 798-802, 20% acceptance rate. 95. 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 (18.5% acceptance rate). 96. 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. 97. Balakrishnan, S. and Madigan, D. (2006). A One-Pass Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets. Bayesian Analysis. 1, 345-362. 98. Madigan, D., Ju, W., Krishnan, P., and Krishnakumar, A.S. (2006). Location estimation in wireless networks: A Bayesian approach. Statistica Sinica, 16, 495-522. 99. Madigan, D., Vardi, Y., and Weissman, I. (2006). Extreme value theory applied to document retrieval from large collections. Information Retrieval, 9, 273-294. 100. Hauben, M., Madigan, D., Gerrits, C.M., Walsh, L., and Van Puijenbroek, E.P. (2005). The role of data mining in pharmacovigilance. Expert Opinion in Drug Safety., 4(5), 929-948. 9 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 10 101. 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. 102. Madigan, D., Genkin, A., Argamon, S., Fradkin, D., and Ye, L. (2005). Author identification. Proceedings of CSNA/Interface 05. 103. Eyheramendy, S. and Madigan, D. (2005). A Novel Feature Selection Score for Text Categorization. International Workshop on Feature Selection for Data Mining, 1-8, 24% acceptance rate. 104. Madigan, D. (2005). Statistics and Data Mining. In: AMS-DIMACS Discrete Methods in Epidemiology, James Abello and Graham Cormode (Editors), 21-24. 105. 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. 106. Madigan, D. (2005). Bayesian data mining for surveillance. In: Spatial and Syndromic Surveillance for Public Health (Andrew Lawson and Ken Kleinman, Editors), 203-221. 107. 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 (17% acceptance rate) 108. Madigan, D. (2004). Statistics and the war on spam. In: Statistics: A Guide to the Unknown, Deb Nolan (Editor), 135-147. 109. 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. 110. 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. 111. Eyheramendy, S., Lewis, David D., and Madigan, David (2003). On the naïve 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. 112. 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. 113. Madigan, D. and Ridgeway, G. (2003). Bayesian data analysis for data mining. In Handbook of Data Mining, N. Ye (Ed.), 103-132.. 114. 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. 115. Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma in Philadelphia schools. Chest, 124 (4): 141S. 116. Mangione, S., Yuen, E., and Madigan, D. (2003). Asthma and tobacco: A survey of 65 Philadelphia middle schools. Chest, 124 (4): 141S-142S. 10 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 11 117. 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. 118. 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. 119. 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. 120. 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. 121. Hoeting, J., Raftery, A.E., and Madigan, D. (2002). A method for simultaneous variable and transformation selection in linear regression. Journal of Computational and Graphical Statistics, 11, 485-507. 122. 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. 123. 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. 124. 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. 125. 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. 126. Andersson, S.A., Madigan, D., and Perlman, M.D. (2001). An alternative Markov property for chain graphs. Scandinavian Journal of Statistics, 28, 33-85. 127. 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. 128. 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. 129. 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. 130. Madigan, D. and Nason, M. (2000). Statistics perspectives on data and knowledge. Handbook of Knowledge Discovery and Data Mining, Oxford University Press. 131. Nason, M. and Madigan, D. (2000). Sampling. Handbook of Knowledge Discovery and Data Mining, Oxford University Press. 11 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 12 132. 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. 133. Hoeting, J.A., Madigan, D., Raftery, A.E., and Volinsky, C.T. (1999). Bayesian model averaging – a tutorial. Statistical Science, 14, 382-401. 134. 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. 135. 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. 136. 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. 137. 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. 138. 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. 139. Ridgeway, G., Madigan, D., Richardson, T., and O’Kane, K. (1998). Interpretable Boosted Naïve Bayes Classification In Proceedings of KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining, 101-104. 140. 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. 141. Draper, D. and Madigan, D. (1997). The scientific value of Bayesian statistical methods. IEEE Intelligent Systems and their Applications, 12, 18-21. 142. Madigan, D. and York, J. (1997). Bayesian methods for estimating the size of a closed population. Biometrika, 84, 19-31. 143. 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. 144. 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. 145. 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. 146. Madigan, D., Mosurski, K., and Almond, R.G. (1997). Explanation in belief networks. Journal of Computational and Graphical Statistics, 6, 160-181. 147. 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. 12 FILED: ERIE COUNTY CLERK 03/17/2023 09:06 PM INDEX NO. 815818/2020 NYSCEF DOC. NO. 340 RECEIVED NYSCEF: 03/17/2023 David Madigan, CV, Page 13 148. 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. 149. 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. 150. 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. 151. 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. 152. Raftery, A.E., Madigan, D., and Volinsky, C.T. (1996). Accounting for mo