These original OMOP resources are no longer supported. You can find the latest developments by visiting the OHDSI website.

Original OMOP Publications

Biostatistics. 2014;15(1):36-9.
Discussion: An estimate of the science-wise false discovery rate and application to the top medical literature.
Schuemie MJ, Ryan PB, Suchard MA, Shahn Z, Madigan D.

Annual Review of Statistics and Its Application. 2014;1(1):11-39.
A Systematic Statistical Approach to Evaluating Evidence from Observational Studies.
Madigan D, Stang PE, Berlin JA, Schuemie M, Overhage JM, Suchard MA, et al.

In E. B. Andrews & Nicholas Moore (Eds.), Mann's Pharmacovigilance, 3rd Edition (2014). Sussex, England: Wiley-Blackwell.
Development and evaluation of infrastructure and analytic methods for systematic drug safety surveillance: Lessons and resources from the Observational Medical Outcomes Partnership (Chapter 28)
Stang P, Ryan P, Hartzema AG, Madigan D, Overhage JM, Welebob E, Reich CG, Scarnecchia T.

CPT Pharmacometrics Syst Pharmacol. 2013;2:e76. Epub 2014/01/23.
Medication-wide association studies.
Ryan PB, Madigan D, Stang PE, Schuemie MJ, Hripcsak G.

Drug Safety, 2013, Vol. 36, Supplement 1 (pp. S1-S204). Studying the Science of Observational Research: Empirical Findings from the Observational Medical Outcomes Partnership.

Stat Med. 2013 Jul 30. doi: 10.1002/sim.5925. [Epub ahead of print]
Interpreting observational studies: why empirical calibration is needed to correct p-values.
Schuemie MJ, Ryan PB, Dumouchel W, Suchard MA, Madigan D.

J Biomed Inform. 2013 Jun 13. pii: S1532-0464(13)00072-5. doi: 10.1016/j.jbi.2013.05.006. [Epub ahead of print]
Developing an expert panel process to refine health outcome definitions in observational data.
Fox BI, Hollingsworth JC, Gray MD, Hollingsworth ML, Gao J, Hansen RA.

Drug Saf. 2013 Aug;36(8):651-61. doi: 10.1007/s40264-013-0060-8.
Assessment of case definitions for identifying acute liver injury in large observational databases.

Katz AJ, Ryan PB, Racoosin JA, Stang PE.

Am J Epidemiol. 2013;178(4):645-51.
Evaluating the Impact of Database
Heterogeneity on Observational Study Results.

Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG, Suchard MA, Dumouchel W, Berlin JA.

Statistics in Biopharmaceutical Research. 2013 28 Apr. DOI:10.1080/19466315.2013.791638
Learning from Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products
Patrick Ryan, Marc A. Suchardb, Martijn Schuemie & David Madigan

Therapeutic Advances in Drug Safety. April 2013 vol. 4 no. 2 53-62. doi: 10.1177/2042098613477445
Does design matter? Systematic evaluation of the impact of analytical choices on effect estimates in observational studies
Madigan D, Ryan PB, Schuemie M.

Res Social Adm Pharm. 2013 Jun 7. pii: S1551-7411(13)00063-6. doi: 10.1016/j.sapharm.2013.04.012. [Epub ahead of print]
Expert panel assessment of acute liver injury identification in observational data.
Hansen RA, Gray MD, Fox BI, Hollingsworth JC, Gao J, Hollingsworth ML, Carpenter DM.

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

ACM Trans Model Comput Simul. 2013;23(1):1-17.
Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model
Marc A. Suchard, Shawn E. Simpson, Ivan Zorych, Patrick Ryan, David Madigan

"A Picture is Worth a Thousand Tables" 2012, pp 391-413
Using Exploratory Visualization in the Analysis of Medical Product Safety in Observational Healthcare Data
Patrick Ryan

Stat Med. 2012 Dec 30;31(30):4401-15. doi: 10.1002/sim.5620. Epub 2012 Sep 27.
Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership.
Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG.

J Biomed Inform. 2012 Aug;45(4):689-96. doi: 10.1016/j.jbi.2012.05.002. Epub 2012 Jun 7.
Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases.
Reich C, Ryan PB, Stang PE, Rocca M.

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence. 1599-1605, Toronto, 2012
Identifying adverse drug events by relational learning.

Page D, Santos Costa V, Natarajan S, Barnard A, Peissig P, and Caldwell M.

Clin Pharmacol Ther. 2012 Jun;91(6):1010-21. doi: 10.1038/clpt.2012.50.
Novel data-mining methodologies for adverse drug event discovery and analysis.
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C.

Health Outcomes Research in Medicine. Volume 3, Issue 1, February 2012, Pages e37–e44.
Health Outcomes of Interest in Observational Data: Issues in Identifying Definitions in the Literature
Stang PE, Ryan PB, Dusetzina SB, Hartzema AG, Reich C, Overhage JM, & Racoosin JA.

J Am Med Inform Assoc. 2012 Jan-Feb;19(1):54-60. doi: 10.1136/amiajnl-2011-000376. Epub 2011 Oct 28.
Validation of a common data model for active safety surveillance research.
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE.

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

AMIA Annu Symp Proc. 2011;2011:1176-85. Epub 2011 Oct 22.
Design and validation of a data simulation model for longitudinal healthcare data.
Murray RE, Ryan PB, Reisinger SJ.

Stat Methods Med Res. 2013 Feb;22(1):39-56. doi: 10.1177/0962280211403602. Epub 2011 Aug 30.
Disproportionality methods for pharmacovigilance in longitudinal observational databases.
Zorych I, Madigan D, Ryan P, Bate A.

Epidemiology. 2011 Sep;22(5):629-31. doi: 10.1097/EDE.0b013e318228ca1d.
What can we really learn from observational studies?: the need for empirical assessment of methodology for active drug safety surveillance and comparative effectiveness research.
Madigan D, Ryan P.

Ann Intern Med. 2010 Nov 2;153(9):600-6. doi: 10.7326/0003-4819-153-9-201011020-00010.
Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.
Stang PE, Ryan PB, Racoosin JA, Overhage JM, Hartzema AG, Reich C, Welebob E, Scarnecchia T, Woodcock J.

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

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.