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  Ayvaz, S., Zhu, Q., Hochheiser, H., Brochhausen, M., Horn, J., Dumontier, M., … Boyce, R. D. (2014). Drug-Drug Interaction Data Source Survey and Linking. AMIA Summits on Translational Science Proceedings, 2014, 16.
    Bell, C., Chakravarty, A., Gruber, S., Heckbert, S. R., Levenson, M., Martin, D., … Walker, A. M. (2014). Characteristics of study design and elements that may contribute to the success of electronic safety monitoring systems. Pharmacoepidemiology and Drug Safety, 23(11), 1223–1225. https://doi.org/10.1002/pds.3712
    DeFalco, F. J., Ryan, P. B., & Soledad Cepeda, M. (2013). Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure. Health Services & Outcomes Research Methodology, 13(1), 58–67. https://doi.org/10.1007/s10742-012-0102-1
    Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., … Shah, N. H. (2014). Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art. Drug safety : an international journal of medical toxicology and drug experience, 37(10), 777–790. https://doi.org/10.1007/s40264-014-0218-z
    Harpaz, R., Odgers, D., Gaskin, G., DuMouchel, W., Winnenburg, R., Bodenreider, O., … Shah, N. H. (2014). A time-indexed reference standard of adverse drug reactions. Scientific Data, 1. https://doi.org/10.1038/sdata.2014.43
    Jager, L. R., & Leek, J. T. (2014). An estimate of the science-wise false discovery rate and application to the top medical literature. Biostatistics, 15(1), 1–12. https://doi.org/10.1093/biostatistics/kxt007
    Makadia, R., & Ryan, P. B. (2014). Transforming the Premier Perspective® Hospital Database into the Observational Medical Outcomes Partnership (OMOP) Common Data Model. eGEMs, 2(1). https://doi.org/10.13063/2327-9214.1110
    Matcho, A., Ryan, P., Fife, D., & Reich, C. (2014). Fidelity Assessment of a Clinical Practice Research Datalink Conversion to the OMOP Common Data Model. Drug Safety, 37(11), 945–959. https://doi.org/10.1007/s40264-014-0214-3
    Ogunyemi, O. I., Meeker, D., Kim, H.-E., Ashish, N., Farzaneh, S., & Boxwala, A. (2013). Identifying Appropriate Reference Data Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems. Medical Care, 51, S45. https://doi.org/10.1097/MLR.0b013e31829b1e0b
    Ohno-Machado, L., Agha, Z., Bell, D. S., Dahm, L., Day, M. E., Doctor, J. N., … Nebeker, J. R. (2014). pSCANNER: patient-centered Scalable National Network for Effectiveness Research. Journal of the American Medical Informatics Association : JAMIA, 21(4), 621–626. https://doi.org/10.1136/amiajnl-2014-002751
    Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012). Validation of a common data model for active safety surveillance research. Journal of the American Medical Informatics Association : JAMIA, 19(1), 54–60. https://doi.org/10.1136/amiajnl-2011-000376
    Rijnbeek, P. R. (2014). Converting to a Common Data Model: What is Lost in Translation? Drug Safety, 37(11), 893–896. https://doi.org/10.1007/s40264-014-0221-4
    Schuemie, M. J., Gini, R., Coloma, P. M., Straatman, H., Herings, R. M. C., Pedersen, L., … Sturkenboom, M. C. J. M. (2013). Replication of the OMOP Experiment in Europe: Evaluating Methods for Risk Identification in Electronic Health Record Databases. Drug Safety: An International Journal of Medical Toxicology and Drug Experience; Auckland, 36(1), S159-69.
    Schuemie, M. J., Ryan, P. B., DuMouchel, W., Suchard, M. A., & Madigan, D. (2014). Interpreting observational studies: why empirical calibration is needed to correct p-values. Statistics in Medicine, 33(2), 209–218. https://doi.org/10.1002/sim.5925
    Vilar, S., Ryan, P. B., Madigan, D., Stang, P. E., Schuemie, M. J., Friedman, C., … Hripcsak, G. (2014). Similarity-based modeling applied to signal detection in pharmacovigilance. CPT: Pharmacometrics & Systems Pharmacology, 3, e137. https://doi.org/10.1038/psp.2014.35
    Weng, C., Li, Y., Ryan, P., Zhang, Y., Liu, F., Gao, J., … Hripcsak, G. (2014). A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Applied Clinical Informatics, 5(2), 463–479. https://doi.org/10.4338/ACI-2013-12-RA-0105
    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: An International Journal of Medical Toxicology and Drug Experience; Auckland, 36(2), 119–134.