wanghaisheng
commented
9 years ago Open wanghaisheng opened 9 years ago
wanghaisheng
commented
9 years ago wanghaisheng
commented
9 years ago wanghaisheng
commented
9 years ago 关注2中的社区论坛，在自己的账号设置里选择感兴趣的讨论区 选择watch 即可
wanghaisheng
commented
9 years ago We will develop open-source tools for large-scale analytics. This will include methods for population-level estimation and patient-level prediction. Our population-level estimation workgroup is focused on developing open-source software for safety surveillance and comparative effectiveness. This will be achieved through large-scale implementations of traditional observational study designs, including cohort, case-control, self-controlled case series, and self-controlled cohort. This group is also designing and implementing other orthogonal analyses to support causal inference, informed by Hill’s causal viewpoints as presented as the proof-of-concept tool HOMER at the 2013 OMOP symposium.
As we release these tools, we will post details on this page, so check back regularly…
A common challenge we all face is developing standard definitions for identifying patients with a particular medical condition or exposed to a specific intervention. Our phenotype workgroup is researching and developing strategies for establishing a standardized, evidence-based approach to constructing algorithms to define disease phenotypes that can be used in observational analytics (as cohort criteria, covariates, and outcomes). The group is exploring the entire continuum of possibilities, from the expert-derived consensus-building approach (e.g. eMERGE) to vocabulary-driven approaches to machine learning techniques applied to clinical sources.
As phenotypes are developed and released, we will post details on this page, so check back regularly…
Our knowledge base workgroup is developing an open-source repository of standardized evidence about drug-outcome relationships from disparate sources, including published literature, product labeling, spontaneous adverse event reporting, and existing bio-medical ontologies. The knowledge base will serve as the primary source to enable the construction of test cases (positive controls and negative controls) to facilitate systematic evaluation of method performance.
As the knowledge base is developed and released, we will post details on this page, so check back regularly…
Reference set library
Our knowledge base workgroup is developing an open-source repository of standardized evidence about drug-outcome relationships from disparate sources, including published literature, product labeling, spontaneous adverse event reporting, and existing bio-medical ontologies. The knowledge base will serve as the primary source to enable the construction of test cases (positive controls and negative controls) to facilitate systematic evaluation of method performance. These positive and negative controls will serve as reference sets for both methodological evaluation, as well as for empirical calibration of unknown effects.
As a reference set is developed and released, we will post details on this page, so check back regularly…
Across the OHDSI community, transforming patient-level observational data into the OMOP common data model is a foundational task every data holder has to perform. The ETL specifications and source code that document the transformation process are invaluable artifacts to promote greater transparency in observational research, and also provide others in the community with useful worked examples to support their own development.
We will post ETLs to the OMOP CDM on this page as they are contributed by the community, so check back regularly…
wanghaisheng
commented
9 years ago OHDSI Publications
Voss E, Makadia R, Matcho A, Ma Q, Knoll C, Schuemie M, DeFalco F, Londhe A, Zhu V, Ryan P. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. Journal of the American Medical Informatics Association Feb 2015.
Boyce RD, Ryan PB, Noren GN, et al. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest. Drug Saf. 2014 Jul 2;2:2.
Weng C, Li Y, Ryan P, et al. A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records. Appl Clin Inform. 2014 May 7;5(2):463-79. doi: 10.4338/ACI-2013-12-RA-0105. eCollection 2014.
Boland MR, Tatonetti NP, Hripcsak G. CAESAR: a Classification Approach for Extracting Severity Automatically from Electronic Health Records. Intelligent Systems for Molecular Biology Phenotype Day. 2014; Boston, MA.
OMOP Publications
Schuemie MJ, Trifiro G, Coloma PM, Ryan PB, Madigan D. Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure-adjusted self-controlled case series. Stat Methods Med Res. 2014 Mar 31;31:31.
Schuemie MJ, Ryan PB, Suchard MA, Shahn Z, Madigan D. Discussion: An estimate of the science-wise false discovery rate and application to the top medical literature. Biostatistics. 2014 Jan;15(1):36-9; discussion 9-45. doi: 10.1093/biostatistics/kxt037. Epub 2013 Sep 25.
Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2014 Jan 30;33(2):209-18. doi: 10.1002/sim.5925. Epub 2013 Jul 30.
Hansen RA, Gray MD, Fox BI, et al. Expert panel assessment of acute liver injury identification in observational data. Res Social Adm Pharm. 2014 Jan-Feb;10(1):156-67. doi: 10.1016/j.sapharm.2013.04.012. Epub Jun 7.
Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics. 2014 Apr;15(2):207-21. doi: 10.1093/biostatistics/kxt043. Epub 2013 Oct 4.
Madigan D, Stang PE, Berlin JA, et al. A Systematic Statistical Approach to Evaluating Evidence from Observational Studies. Annual Review of Statistics and Its Application. 2014;1(1):11-39.
Paul Stang PR, Abraham G. Hartzema, David Madigan, J Marc Overhage, Emily Welebob, Christian G. Reich, Thomas Scarnecchia. Development and Evaluation of Infrastructure and Analytic Methods for Systematic Drug Safety Surveillance: Lessons and Resources from the Observational Medical Outcomes Partnership. In: Elizabeth B. Andrews NM, editor. Mann’s Pharmacovigilance. Third ed: Wiley-Blackwell; 2014. p. 866.
Schuemie MJ, Madigan D, Ryan PB. Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S133-42. doi: 10.1007/s40264-013-0107-x.
Madigan D, Schuemie MJ, Ryan PB. Empirical performance of the case-control method: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S73-82. doi: 10.1007/s40264-013-0105-z.
Cepeda MS, Fife D, Ma Q, Ryan PB. Comparison of the risks of opioid abuse or dependence between tapentadol and oxycodone: results from a cohort study. J Pain. 2013 Oct;14(10):1227-41. doi: 10.016/j.jpain.2013.05.010. Epub Jul 10.
Ryan PB, Madigan D, Stang PE, Schuemie MJ, Hripcsak G. Medication-wide association studies. CPT Pharmacometrics Syst Pharmacol. 2013 Sep 18;2:e76.(doi):10.1038/psp.2013.52.
Reich CG, Ryan PB, Suchard MA. The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S195-204. doi: 10.1007/s40264-013-0112-0.
Reich CG, Ryan PB, Schuemie MJ. Alternative outcome definitions and their effect on the performance of methods for observational outcome studies. Drug Saf. 2013 Oct;36(Suppl 1):S181-93. doi: 10.1007/s40264-013-0111-1.
Ryan PB, Schuemie MJ. Evaluating performance of risk identification methods through a large-scale simulation of observational data. Drug Saf. 2013 Oct;36(Suppl 1):S171-80. doi: 10.1007/s40264-013-0110-2.
Ryan PB, Stang PE, Overhage JM, et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 2013 Oct;36(Suppl 1):S143-58. doi: 10.1007/s40264-013-0108-9.
DuMouchel W, Ryan PB, Schuemie MJ, Madigan D. Evaluation of disproportionality safety signaling applied to healthcare databases. Drug Saf. 2013 Oct;36(Suppl 1):S123-32. doi: 10.1007/s40264-013-0106-y.
Noren GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigan D. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S107-21. doi: 10.1007/s40264-013-0095-x.
Ryan PB, Schuemie MJ, Madigan D. Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S95-106. doi: 10.1007/s40264-013-0101-3.
Suchard MA, Zorych I, Simpson SE, Schuemie MJ, Ryan PB, Madigan D. Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S83-93. doi: 10.1007/s40264-013-0100-4.
Ryan PB, Schuemie MJ, Gruber S, Zorych I, Madigan D. Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug Saf. 2013 Oct;36(Suppl 1):S59-72. doi: 10.1007/s40264-013-0099-6.
Hartzema AG, Reich CG, Ryan PB, et al. Managing data quality for a drug safety surveillance system. Drug Saf. 2013 Oct;36(Suppl 1):S49-58. doi: 10.1007/s40264-013-0098-7.
Ryan PB, Schuemie MJ, Welebob E, Duke J, Valentine S, Hartzema AG. Defining a reference set to support methodological research in drug safety. Drug Saf. 2013 Oct;36(Suppl 1):S33-47. doi: 10.1007/s40264-013-0097-8.
Stang PE, Ryan PB, Overhage JM, Schuemie MJ, Hartzema AG, Welebob E. Variation in choice of study design: findings from the Epidemiology Design Decision Inventory and Evaluation (EDDIE) survey. Drug Saf. 2013 Oct;36(Suppl 1):S15-25. doi: 10.1007/s40264-013-0103-1.
Overhage JM, Ryan PB, Schuemie MJ, Stang PE. Desideratum for evidence based epidemiology. Drug Saf. 2013 Oct;36(Suppl 1):S5-14. doi: 0.1007/s40264-013-0102-2.
Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics. 2013 Dec;69(4):893-902. doi: 10.1111/biom.12078. Epub 2013 Oct 11.
Katz AJ, Ryan PB, Racoosin JA, Stang PE. Assessment of case definitions for identifying acute liver injury in large observational databases. Drug Saf. 2013 Aug;36(8):651-61. doi: 10.1007/s40264-013-0060-8.
Madigan D, Ryan PB, Schuemie M, et al. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol. 2013 Aug 15;178(4):645-51. doi: 10.1093/aje/kwt010. Epub 2013 May 5.
Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG. Response to comment on ‘empirical assessment of methods for risk identification in healthcare data’. Stat Med. 2013 Mar 15;32(6):1075-7. doi: 10.02/sim.5725.
Defalco FJ, Ryan PB, Soledad Cepeda M. Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure. Health Serv Outcomes Res Methodol. 2013 Mar;13(1):58-67. Epub 2012 Oct 27.
Evans SJ. Moving along the yellow brick (card) road? Drug Saf. 2013 Oct;36(Suppl 1):S3-4. doi: 10.1007/s40264-013-0096-9.
Hansen RA, Gray MD, Fox BI, Hollingsworth JC, Gao J, Zeng P. How well do various health outcome definitions identify appropriate cases in observational studies? Drug Saf. 2013 Oct;36(Suppl 1):S27-32. doi: 10.1007/s40264-013-0104-0.
Schuemie MJ, Gini R, Coloma PM, et al. Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases. Drug Saf. 2013 Oct;36(Suppl 1):S159-69. doi: 10.1007/s40264-013-0109-8.
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. J Biomed Inform. 2013 Oct;46(5):795-804. doi: 10.1016/j.jbi.2013.05.006. Epub Jun 13.
Zorych I, Madigan D, Ryan P, Bate A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res. 2013 Feb;22(1):39-56. doi: 10.1177/0962280211403602. Epub 2011 Aug 30.
Ryan P, Suchard MA, Schuemie M, Madigan D. Learning From Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products. Statistics in Biopharmaceutical Research. 2013;5(3).
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. 2013 April 1, 2013;4(2):53-62.
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. Statistical challenges in systematic evidence generation through analysis of observational healthcare data networks. Statistical Methods in Medical Research. 2013;22(1):3-6.
Overhage JM, Overhage LM. Sensible use of observational clinical data. Statistical Methods in Medical Research. 2013;22(1):7-13.
Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012 Dec 30;31(30):4401-15. doi: 10.1002/sim.5620. Epub 2012 Sep 27.
Reich C, Ryan PB, Stang PE, Rocca M. Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases. J Biomed Inform. 2012 Aug;45(4):689-96. doi: 10.1016/j.jbi.2012.05.002. Epub Jun 7.
Gagne JJ, Fireman B, Ryan PB, et al. Design considerations in an active medical product safety monitoring system. Pharmacoepidemiol Drug Saf. 2012 Jan;21(Suppl 1):32-40. doi: 10.1002/pds.2316.
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012 Jan-Feb;19(1):54-60. doi: 10.1136/amiajnl-2011-000376. Epub 2011 Oct 28.
Page D, Costa VS, Natarajan S, Barnard A, Peissig P, Caldwell M. Identifying Adverse Drug Events by Relational Learning. Proc Conf AAAI Artif Intell. 2012 Jul;2012:790-3.
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012 Jun;91(6):1010-21. doi: 10.38/clpt.2012.50.
Ryan P. Using Exploratory Visualization in the Analysis of Medical Product Safety in Observational Healthcare Data. In: Krause A, OConnell, Michael editor. A Picture is Worth a Thousand Tables: Springer; 2012. p. 429.
Stang PE, Ryan PB, Dusetzina SB, et al. Health Outcomes of Interest in Observational Data: Issues in Identifying Definitions in the Literature. Health Outcomes Research in Medicine. 2012 2//;3(1):e37-e44.
Murray RE, Ryan PB, Reisinger SJ. Design and validation of a data simulation model for longitudinal healthcare data. AMIA Annu Symp Proc. 2011;2011:1176-85. Epub 2011 Oct 22.
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 10 Oct 13.
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 Sep;22(5):629-31. doi: 10.1097/EDE.0b013e318228ca1d.
Stang PE, Ryan PB, Racoosin JA, 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. doi: 10.7326/0003-4819-153-9-201011020-00010.
Reisinger SJ, Ryan PB, O’Hara DJ, et al. Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases. J Am Med Inform Assoc. 2010 Nov-Dec;17(6):652-62. doi: 10.1136/jamia.2009.002477.
Ryan P, Welebob E, Hartzema A, Stang P, Overhage JM. Surveying US Observational Data Sources and Characteristics for Drug Safety Needs. Pharm Med. 2010 2010/08/01;24(4):231-8.
wanghaisheng
commented
9 years ago * May 23, 2014 *
_Dynamic Product Labeling – Jon Duke, Regenstrief Institute _
Uppsala Monitoring Centre Research Conference 2014 - Uppsala, Sweden **
Presentation Slides (starts at slide 42)
* May 23, 2014 *
***The complementary roles of population-level estimation and patient-level prediction in pharmacovigilance -* Patrick Ryan, Janssen R&D ****
Uppsala Monitoring Centre Research Conference 2014 - Uppsala, Sweden
* June 13, 2014 *
_Opportunity for real-world evidence from large-scale analysis of observational health data – Patrick Ryan, Janssen R&D _
AAAS, Evidence for New Medical Products: Implications for Patients and Health Policy – Washington, DC **
Video Presentation (begins at 40:30)
* June 7, 2014 *
_Establishing an Open Source Informatics Framework to enable Observational Health Data Sciences - Frank DeFalco, Janssen R&D; Patrick Ryan, Janssen R&D; and Christopher Knoll, Janssen R&D _
_4th Annual EDM Forum Stakeholder Symposium - San Diego, CA _
* May 15, 2014 *
_OHDSI Overview and OMOP Common Data Model Development – Patrick Ryan, Janssen R&D and Christian Reich, AstraZeneca _
_IMEDS Community Meeting Webinar _
OHDSI Presentation with AudioOMOP CDM v5 Draft Presentation with Audio
* April 26, 2014 *
_Learning From Observational Healthcare Data: Lessons from the Observational Medical Outcomes Partnership _
3rd Workshop on Data Mining for Medicine and Healthcare, SIAM International Conference on Data Mining – Philadelphia, PA **
* March 18, 2014 *
_Learning From Observational Healthcare Data: Lessons from the Observational Medical Outcomes Partnership _
_ASCPT Annual Meeting; Next-Generation Clinical Pharmacology: Integrating Systems Pharmacology, Data-Driven Therapeutics, and Personalized Medicine – Atlanta, GA _
wanghaisheng
commented
9 years ago Funding opportunities:
Publication opportunities:
Conference opportunities:
1、WIKI
2、社区论坛
3、邮件列表
4、各种库
5、发表的文章
6、PPT
7、合作机会Collaborator Opportunities