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Publications - FNIH OMOP Partnership Archive

OMOP Publications

Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG, et al. Evaluating the Impact of Database Heterogeneity on Observational Study Results. American journal of epidemiology. May 5, 2013. DOI: 10.1093/aje/kwt010

Fox BI, Hollingsworth JC, Gray MD, Hollingsworth ML, Gao J, Hansen RA. Developing an expert panel process to refine health outcome definitions in observational data. Journal of Biomedical Informatics. 2013: In press.

Ryan PB, Suchard MA, Schuemie M & Madigan D (2013): Learning from Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products, Statistics in Biopharmaceutical Research, DOI:10.1080/19466315.2013.791638

Madigan D, Ryan PB, Schuemie M. Does design matter? Systematic evaluation of the impact of analytical choices on effect estimates in observational studies. Therapeutic Advances in Drug Safety. February 25, 2013 2013.

Hansen RA, Gray M, Fox BI, Hollingsworth J, Gao J, Hollingsworth M, Carpenter DM. Expert panel assessment of acute liver injury identification in observational data. Research in Social and Administrative Pharmacy 2012 (in press).

Statistical Methods in Medical Research. February 2013; 22 (1). Special Issue: Effectiveness Research. Guest editors: Xiaochun Li, Lingling Li and Patrick Ryan.

Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D. Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model. ACM Trans Model Comput Simul. 2013;23(1):1-17.

Ryan, P. B. (2012). Using exploratory visualization in the analysis of medical product safety in observational healthcare data. In A. Krause & M. O’Connell (Eds.), A picture is worth a thousand tables: Graphics in life sciences (pp. 391-413). New York, New York: Springer-Verlag.

Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA and Hartzema AG. (2012), Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Statist. Med. doi: 10.1002/sim.5620.

Reich C, Ryan PB, Stang PE, Rocca M. Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases. Journal of Biomedical Informatics 2012; 45: 689-696.

Page D, Santos Costa V, Natarajan S, Barnard A, Peissig P, and Caldwell M. Identifying adverse drug events by relational learning. In AAAI-12, pages 1599-1605, Toronto, 2012.

Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis. Clinical Pharmacology & Therapeutics (2 May 2012); doi:10.1038/clpt.2012.50

Stang PE, Ryan PB, Dusetzina SB, Hartzema AG, Reich C, Overhage JM, & Racoosin JA. Health Outcomes of Interest in Observational Data: Issues in Identifying Definitions in the Literature. Health Outcomes Research in Medicine (2011). doi: 10.1016/j.ehrm.2011.11.003

Overhage JM, Ryan PB, Reich CG et al. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54-60. Epub 2011 Oct 28.

Schuemie MJ. Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD. Pharmacoepidemiol Drug Saf. 2011 Mar;20(3):292-9. doi: 10.1002/pds.2051. Epub 2010 Oct 13.

Murray RE, Ryan PB, & Reisinger SJ. (2011). Design and Validation of a Data Simulation Model for Longitudinal Healthcare Data. AMIA Annu Symp Proc., USA, 2011: 1176–1185.

Zorych I, Madigan D, Ryan P, and Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases.
Stat Methods Med Res, August 30, 2011 as doi:10.1177/0962280211403602.

Madigan D, Ryan P. 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. 2011;22:629–631.

Stang PE, Ryan PB, Racoosin JA, Overhage JM, Hartzema AG, Reich C, et al. Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med. 2010 Nov 2;153(9):600-6.

Ryan PB, Welebob E, Hartzema AG, Stang PE, Overhage JM. Surveying US observational data sources and characteristics for drug safety needs. Pharm Med. 2010; 24 (4): 231-238.

Publications of Interest

Zhou, X., Murugesan, S., Bhullar, H., Liu, Q., Cai, B., Wentworth, C., Bate A. (2013) An Evaluation of the Thin Database in the Omop Common Data Model for Active Drug Safety Surveillance. Drug Safety: 1-16. DOI: 10.1007/s40264-012-0009-3.

DeFalco F, Ryan P, Soledad Cepeda M (2012) Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure. Health Services and Outcomes Research Methodology: 1-10. DOI 10.1007/s10742-012-0102-1.

Harpaz R, Vilar S, DuMouchel W, Salmasian H, Haerian K, et al. (2012) Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. Journal of the American Medical Informatics Association. DOI: 10.1136/amiajnl-2012-000930.

Kahn MG, Batson D, Schilling LM (2012) Data model considerations for clinical effectiveness researchers. Med Care 50 Suppl: S60-67.

Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF (2012) A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care 50 Suppl: S21-29.

Schuemie MJ, Coloma PM, Straatman H, Herings RM, Trifiro G, et al. (2012) Using Electronic Health Care Records for Drug Safety Signal Detection: A Comparative Evaluation of Statistical Methods. Med Care.

Platt, R. and Carnahan, R. (2012), The U.S. Food and Drug Administration's Mini-Sentinel Program. Pharmacoepidem. Drug Safe., 21: 1–303. doi: 10.1002/pds.3230

Robb, M. A., Racoosin, J. A., Sherman, R. E., Gross, T. P., Ball, R., Reichman, M. E., Midthun, K. and Woodcock, J. (2012), The US Food and Drug Administration's Sentinel Initiative: Expanding the horizons of medical product safety. Pharmacoepidem. Drug Safe., 21: 9–11. doi: 10.1002/pds.2311

Curtis, L. H., Weiner, M. G., Boudreau, D. M., Cooper, W. O., Daniel, G. W., Nair, V. P., Raebel, M. A., Beaulieu, N. U., Rosofsky, R., Woodworth, T. S. and Brown, J. S. (2012), Design considerations, architecture, and use of the Mini-Sentinel distributed data system. Pharmacoepidem. Drug Safe., 21: 23–31. doi: 10.1002/pds.2336

Duke J, Friedlin J, Ryan, P. A Quantitative Analysis of Adverse Events and "Overwarning" in Drug Labeling. Arch Intern Med.2011; 171: 944-946

Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the Sentinel System - A national resource for evidence development. N Engl J Med 2011;364:498-499

Coloma PM, Schuemie MJ, Trifiro G. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiology and Drug Safety 2011; 20: 1-11

Brookhart, M.A., Sturmer, T., Glynn, R.J., Rassen, J., and Schneeweiss, S. (2010). Confounding control in healthcare database research: challenges and potential approaches. Medical Care, 48, S114-S120.

Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care 2010;48:Suppl:S45-S51

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 Anaysis and Data Mining, 3, 197-208.

Brown, J. S., M. Kulldor , et al. (2009). Early adverse drug event signal detection within population-based health networks using sequential methods: key methodologic considerations. Pharmacoepidemiology and Drug Safety DOI: 10.1002/pds.1706.

Li, L. (2009). A conditional sequential sampling procedure for drug safety surveillance. Statistics in Medicine. DOI:10.1002/sim.3689

Platt R, Wilson M, Chan KA, Benner JS, Marchibroda J, McClellan M. The new Sentinel Network -- improving the evidence of medical-product safety. N Engl J Med 2009;361:645-647

Curtis JR, Cheng H, Delzell E, Fram D, Kilgore M, Saag K, Yun H and DuMouchel W. (2008). Adaptation of Bayesian data mining algorithms to longitudinal claims data. Medical Care, 46, 969-975.

Jin, H., Chen, J., He, H., Williams, G.J., Kelman, C., and O Keefe, C.M. (2008). Mining unexpected temporal associations: Applications in detecting adverse drug reactions. IEEE Transactions on Information Technology in Biomedicine, 12, 488-500.

Noren, G. N., Bate, A., Hopstadius, J., Star, K., and Edwards, I. R. (2008). Temporal pattern discovery for trends and transient e ects: its application to patient records. In: Proceedings of the Fourteenth International Conference on Knowledge Discovery and Data Mining SIGKDD 2008, 963-971.

Lieu TA, Kulldor M, Davis RL, Lewis EM, Weintraub E, Yih K, Yin R, Brown JS, and Platt R. (2007). Real-time vaccine safety surveillance for the early detection of adverse events. Medical Care, 45, S89-95.