Resources

If you would like to add a resource, please contact OMOP.

Publications

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.

Links

WEBSITE LINKS
Reagan-Udall Foundation for the FDA
Food and Drug Administration Amendment Act (FDAAA)
Mini-Sentinel
FDA Sentinel Initiative
Vaccine Safety Datalink (VSD)
Exploring & Understanding Adverse Drug Reactions (EU-ADR)
Drug Safety and Effectiveness Network (DSEN)
Innovative Medicines Initiative - Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (IMI-PROTECT)
Brookings Institution's activities on active medical product surveillance.

Presentations

OMOP Presentations

Download

Real World Evidence and Pharmaceuticals by Christian Reich
PRISME, Boston, MA

October 16, 2013

An Empirical Approach to Measuring and Calibrating for Error in Observational Analyses by Patrick Ryan
IOM Roundtable on Value & Science-Driven Health Care, Washington, DC

September 26, 2013

29th International Conference on Pharmacoepidemiology & Therapeutic Risk Management (ICPE), Montréal, Canada, August 2013.
1. Harmonizing Methods and Data across Multiple Databases: Lessons from OMOP
2. Large-scale regularized regression for identifying appropriate treatment comparisons for comparative effectiveness research

3. Why do we need to measure performance and how should we do this? Challenges and trade-offs in creating the OMOP reference set
4. Impact of reference set on performance of risk identification methods: EU-ADR vs. OMOP

Click on each individual presentation to download

Perspectives on Data Quality Assessment by Patrick Ryan
AcademyHealth - 2013 Annual Research Meeting, Baltimore, MD

June 23, 2013

How the OMOP Common Data Model Enables Standardized Analytics by Patrick Ryan
2013 EDM Forum Stakeholder Symposium, Baltimore, MD

June 22, 2013

Empirical learning from observational analyses: OMOP Lessons by Patrick Ryan
Signal Detection & Interpretation in Pharmacovigilance, London, UK

June 12, 2013

Analysis of Longitudinal Databases by Patrick Ryan
Signal Detection & Interpretation in Pharmacovigilance pre-conference tutorial, London, UK

June 11, 2013

IMEDS Overview by Troy McCall
Webinar presentation

June 6, 2013

YouTube video

OMOP Research Results by William DuMouchel
MidWest Biopharmaceutical Statistics Workshop, Muncie, Indiana

May 21, 2013

Challenges of Analyzing OMOP Results and Some Visual Approaches, by Rebecca Ferrell, University of Washington
Statistical and Applied Mathematical Sciences Institute (SAMSI) Workshop, Research Triangle Park, NC

May 9, 2013

Analysis of the OMOP Results Database: Does the Method Matter More than the Truth? by Alan F. Karr
National Institute of Statistical Sciences

Georgetown University, Washington, DC

April 26, 2013

An Empirical Approach to Measuring and Calibrating for Error in Observational Analyses by Patrick Ryan
Institute of Medicine, Washington, DC

April 25, 2013

Cross Industry Data Partnerships and OMOP by Christian Reich
2013 Medical Informatics World Conference, Boston, MA

April 8, 2013

OMOP Common Data Model, SAFTINet and ROSITA by Lisa Schilling
2013 AMIA Summit on Clinical Research Informatics, San Francisco, CA

March 22, 2013

Perspectives on Data Quality Assessment: OMOP Lessons Learned by Patrick Ryan
2013 AMIA Summit on Clinical Research Informatics, San Francisco, CA

March 22, 2013

Advancing the Science of a Risk Identification System: Research Findings from OMOP by Abraham Hartzema and Patrick Ryan
ISPE Webinar

October 23, 2012

Lessons from the Observational Medical Outcomes Partnership: Opportunities for Exploring Healthcare Databases to Study the Effects of Medical Products by Patrick Ryan
Statistical & Applied Mathematical Sciences Institute (SAMSI) - Workshop 2012-13 Program on Data-Driven Decisions in Healthcare

August 29, 2012

Highlights from the OMOP 2012 Annual Symposium presented by Patrick Ryan and Martijn Schuemie
Brookings Roundtable On Active Medical Product Surveillance, Webinar

August 8, 2012

Audio

Active surveillance on medical observational databases presented by Ivan Zorych
2012 Joint Statistical Meetings, San Diego, California

August 2, 2012

2012 OMOP Symposium presentations and audio
Bethesda, Maryland

June 28, 2012

Opportunities for Leveraging National Surveillance Research Efforts to Improve Industry Pharmacovigilance Operations by Patrick Ryan
DIA 2012, Philadelphia, PA

June 27, 2012

Opportunities for a Risk Identification System: Lessons from the Observational Medical Outcomes Partnership by Patrick Ryan
DIA 2012, Philadelphia, PA

June 26, 2012

Beyond relative risk: A drug safety framework for exploring causal effects in observational data presented by Patrick Ryan
Causality Assessment in an Evolving Pharmacovigilance Landscape, Uppsala, Sweden

May 24, 2012

Patient-centered observational analytics: New directions toward studying effects of medical products presented by Patrick Ryan
PRISME Forum Association, Cheshire, UK

May 22, 2012

Beyond relative risk: A drug safety framework for exploring causal effects in observational data presented by Patrick Ryan
FDA / DIA Statistics Forum 2012, Bethesda, MD

April 24, 2012

Opportunities for exploratory visualization in observational data analytics presented by Patrick Ryan
ISPE Mid-Year Meeting, Miami Beach, FL

April 23, 2012

Informatics opportunities in exploring electronic health records: Lessons from the Observational Medical Outcomes Partnership presented by Patrick Ryan
EMBL-EBI Industry Programme Workshop: The growing role of Electronic Medical Records in translational bioinformatics, Hinxton, UK

February 29, 2012

Create Value from Data, Governance, Ethics and Appropriate Use presented by Thomas Scarnecchia
HIMSS 2012 Annual Conference - Secondary Use of Data Symposium

February 20, 2012

Statistical Methods for Drug Safety Surveillance by David Madigan
The Wharton School, University of Pennsylvania

December 7, 2011

Introduction to the OMOP Common Data Model Version 3.0 presented by Patrick Ryan and Lisa Schilling

December 2, 2011 Slides

Watch the Presentation on the OMOP site

New Paradigms in Clinical Trial Methodology Symposium by David Madigan
Research Triangle, NC

November 17, 2011

HIMSS Life Sciences Roundtable - Advancing the Science of Observational Data Analysis in Life Science by Marc Overhage

November 16, 2011

Slides only

Audio / Slides

AcademyHealth Electronic Data Methods Forum Symposium- Opportunities for Standardized Observational Analytics to Support Causal Inference in Comparative Effectiveness Research by Patrick Ryan

October 27, 2011

AMIA 2011 Annual Symposium - Design and Validation of a Data-simulation Model for Longitudinal Healthcare Data by Rich Murray and Patrick Ryan

October 26, 2011

Query Health - “Summer Concert Series" on Distributed Population Queries. The Observational Medical Outcomes
Partnership: Demonstration of distributed population queriespresented by Tom Scarnecchia, Patrick Ryan and Marc Overhage

August 29, 2011

32nd Annual Conference of the International Society for Clinical Biostatistics (ISCB); Ottawa, Canada. Empirical Performance of Large-scale Analysis Methods for Active Surveillance: Lessons from the OMOP presented by Patrick Ryan

August 25, 2011

ISPE 2011, 27th International Conference on Pharmacoepidemiology and Therapeutic Risk Management. "Drug Induced Liver Injury: Latest Developments on Premarket Regulatory Guidance and Approaches to Postmarket Surveillance presented by Judy Racoosin

August 16, 2011

ISPE 2011, 27th International Conference on Pharmacoepidemiology and Therapeutic Risk Management. "Methods Development in Active Drug Safety Surveillance Highlighting the OMOP Findings presented by Abraham Hartzema, Patrick Ryan and Paul Stang

August 16, 2011

2011 Joint Statistical Meetings. Bayesian Logistic Regression For Medical Claims Data Using CPU And GPU presented by Ivan Zorych

August 2, 2011

DIA 2011 Annual Meeting. OMOP: Overview of Experimental Results presented by David Madigan

June 20, 2011

Drug Safety Research Unit (DSRU) 6th Biennial Conference - Signal Detection and Interpretation in Pharmacovigilance, London, UK, presented by Patrick Ryan and David Madigan - June 2011
1. Signal Detection in Observational Databases - David Madigan
2. Case Studies and Other Issues - David Madigan
3. Analysis of Longitudinal Databases - Patrick Ryan
4. The Observational Medical Outcomes Partnership (OMOP): Overview of Experimental Results - Patrick Ryan

Click on each individual presentation to download

OMOP Poster at the 3rd DEcIDE Methods Symposium 2011: Methods for Developing and Analyzing Clinically Rich Data for Patient-Centered Outcomes Research. OMOP Poster: An Empiric Exploration of Analytical Methods and Outcome Definitions for Patient Centered Outcomes Research

June 6, 2011

FDA/PhRMA Biostatistics, Data Management, Scientific Programming Leaders Meeting, FDA, Rockville, MD
Results and Lessons from OMOP presented by Patrick Ryan and David Madigan - April 14, 2011

1. OMOP Overview
2. OMOP Parameter Sensitivity
3. OMOP Discussion - Bayesian Framework

Click on each individual presentation to download

AcademyHealth, Electronic Data Methods Forum Symposium, Washington, DC: OMOP Results and Highlights presented by David Madigan

April 09, 2011

International Society for Pharmacoepidemiology (ISPE) 2011 Mid-Year Meeting: OMOP Results and Perspectives presented by Patrick Ryan

April 09, 2011

Brookings Roundtable: Highlights from the OMOP Symposium presented by Patrick Ryan

March 17, 2011

Post-Market Safety Session of the 2nd Annual FDA / DIA Computational Science Meeting, Washington DC, Active Surveillance Methods Performance and Challenges: Lessons from OMOP presented by Patrick Ryan

March 14, 2011

Pharmacovigilance and Risk Management Strategies 2011- DIA panel: Analysis for Health Care Data presented by Jonathan Morris, MD (UnitedBioSource)

January 10, 2011

Pharmacovigilance and Risk Management Strategies 2011- DIA panel: Analysis for Health Care Data presented by Thomas Scarnecchia

January 10, 2011

PRISM Forum Special Interest Group 2010 - Informatics Opportunities for Exploring the Real-World Effects of Medical Products: Lessons from the Observational Medical Outcomes Partnership presented by Patrick Ryan

October 19, 2010

The Spotfire Life Science Forum 2010 - Visualization opportunities for systematic analyses of observational healthcare data: lessons from the Observational Medical Outcomes Partnership presented by Patrick Ryan

October 5, 2010

Brookings Event - Methods for Signal Refinement in Active Medical Product Surveillance
Exploring Methodological Needs for Signal Refinement presented by Patrick Ryan

September 21, 2010

26th ICPE: International Conference on Pharmacoepidemiology & Therapeutic Risk Management - Implementing & Evaluating Standard Cohort Definitions, Steph Reisinger and Patrick Ryan

August 21, 2010

OMOP Method Evaluation Discussion presented to the FDA by David Madigan and Patrick Ryan

August 09, 2010

2010 Joint Statistical Meeting - The Role of Statistics and Opportunities for Statisticians in Active Drug Safety Surveillance presented by Patrick Ryan

August 01, 2010

2010 Joint Statistical Meeting - OMOP Methods Development presented by David Madigan

August 01, 2010

WorldPharma 2010 - OMOP Methods Overview & Insights presented by Abraham G. Hartzema

July 19, 2010

Brookings Roundtable on Active Medical Product Surveillance - OMOP Methods Overview & Progress presented by David Madigan & Patrick Ryan

July 1, 2010

46th Annual DIA Meeting 2010 - The Role of Statistics & Opportunities for Statisticians in Active Drug Safety Surveillance presented by Patrick Ryan

June 14, 2010

46th Annual DIA Meeting 2010 - OMOP Overview & Progress presented by
Thomas Scarnecchia

June 14, 2010

OMOP Extended Consortium Overview presented by Patrick Ryan

April 22, 2010

ISPE Mid-Year Symposium - April 12, 2010
1. OMOP Overview presented by Thomas Scarnecchia
2. Applying the OMOP Common Data Model Across Administrative Claims and Electronic Health Records presented by Patrick Ryan
3. Use of Standardized Terminologies across an Active Surveillance Network presented by Christian Reich
4. Methods Development: OMOP Status presented by David Madigan

Click on each individual presentation to download

OMOP: Overview of Methods Development and Evaluation Overview & Progress presented at the FDA
David Madigan and Patrick Ryan

January 12, 2010

OMOP Overview & Progress presented at the FDA
Paul Stang

January 12, 2010

DIA Conference on Signal Detection & Data Mining
Patrick Ryan & David Madigan

November 17, 2009

M2009 Data Mining Conference
Patrick Ryan

October 26, 2009

Institute of Medicine of the National Academies
Community Update: Improving the Science of Drug Safety
Thomas Scarnecchia

September 2, 2009

DIA Annual Meeting
Patrick Ryan

June 23, 2009
Download Podcast (m4a format)
Slide Deck Only

Midwest Biopharmaceutical Statistics Workshop
Patrick Ryan

May 17, 2009

White Papers

The purpose of OMOP white papers is to inform you on the processes and decisions or the way that we choose to go for a particular problem or question during the OMOP research. It is not to demonstrate best practice, but to give OMOP readers a single place to be informed while the research is in progress. From time-to-time updates and new papers will be posted.

If you have any questions or feedback on the white papers, please contact OMOP.

OMOP White Papers

Review of Observational Analysis Methods - Feb 2009
An important component of the OMOP effort is to develop a central repository of potential methods and their characteristics to facilitate the structure and development of protocol concepts. An initial list was developed by soliciting contributions and supporting publications from a workgroup, informal literature reviews, and informal reviews of presentations at relevant meetings. These findings are reported with the Review of Observational Analysis Methods and Methods Matrix.

Review of Observational Analysis Methods

Methods Matrix
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Establishing a Drug Era Persistence Window for Active Surveillance - January 2010
The construct "Drug eras" are time periods of continuous drug exposure used in active drug safety surveillance. This paper discusses how OMOP went about to define the optimal window of persistence using two scenarios, a 0-day to a 30-day tolerance window of persistence.


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Establishing a Condition Era Persistence Window for Active Surveillance - January 2010
The construct of ‘condition era’ tables is a means to systematically apply consistent rules for all medical conditions to infer distinct episodes of care from available information, such as diagnosis codes and problem lists. One important decision to make when applying condition era logic to an observational database (administrative claims or electronic health record) is to define the persistence window, or the period of time when two related conditions would be considered part of the same episode of care. This analysis evaluates the impact of the 0-day and 30-day persistence window on the construction of condition eras across the central databases within the OMOP data community.


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Defining a Reference Set for Evaluating the Performance of Active Surveillance Methods - January 2010
The OMOP research requires a reference set to provide a pre-defined benchmark to base comparisons of methods against. In this context, the reference set is a list of test cases of drug-condition pairs classified dichotomously as ‘true associations’ or ‘negative controls’. Methods will be executed against these test cases, and the estimates derived from the methods will be compared to the benchmark. This paper describes the OMOP reference set used for each analysis program and the rationale that was used to develop them.

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Selecting Comparators in Active Surveillance Analyses - March 2010
The OMOP research requires a set of comparator drugs. The desire is to construct a comparator which is sufficiently similar to the target group that effects observed may be plausibly attributable to the exposure of interest. Analysts take great pains to define study inclusion / exclusion criteria when extracting the data and apply additional analysis techniques (such as matching, stratification, and multivariate modeling) to minimize effects of confounding and other biases. Such design decisions are pivotal to the final result, but can be resource intensive and require subjective expert opinion. This white paper proposes ways to develop and evaluate alternative approaches to comparator selection.

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Applying Natural Language Processing to Extract Codify Adverse Drug Reaction in Medication Labels - August 2010
Authors: Jeffrey Friedlin, D.O and Jon Duke, M.D.
Institutions: Indiana University School of Medicine and Regenstrief Institute Indianapolis, Indiana 46202

Medical informaticists developed an automated method of identifying, extracting and codifying adverse drug reaction data contained in the Structured Product Labels for drugs to be studied as part of the OMOP project.

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OMOP Distributed Partner Research Reports - February 2011
Authors: OMOP Distributed Research Partners

The study reports were produced by each OMOP Distributed Research Partner to document and discuss site specific aspects of the OMOP research and findings. Each report also includes discussion regarding the performance of methods and lessons learned. Click on the Distributed Research Partner to download their study report.

1. Department of Veterans Affairs Center for Medication Safety / Outcomes Research, Pharmacy Benefits Management Services

2. Humana, Inc.

3. Partners Healthcare System

4. Regenstrief Institute / Indiana University

5. SDI Health

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Exploration of Four Outcomes: Outcomes and labeling information, in conjunction with other evidence - May 2011
Authors: Jeffrey Friedlin, D.O and Jon Duke, M.D.
Institutions: Indiana University School of Medicine and Regenstrief Institute Indianapolis, Indiana 46202