Martijn Schuemie, PhD
Research Fellow, Global Epidemiology Organization
Johnson & Johnson
Martijn Schuemie started out with a Master’s in Economics, focusing on Information Management, before earning a PhD in Computer Science. His research explored virtual reality systems for treating phobias, focusing on optimizing human-compute interaction He began his academic career at Erasmus University Medical Center in Rotterdam, first working on text mining scientific literature to support molecular biology research. He later shifted into pharmacoepidemiology, contributing to the EU-ADR project, which aimed to detect drug safety signals using observational data.
In 2012, Martijn received a one-year fellowship from the FDA and spent time at Columbia University as an OMOP researcher. The following year, he moved to the private sector, joining Johnson & Johnson to continue his research in OMOP and later in OHDSI. He has also held academic roles, including as an honorary assistant professor at Hong Kong University and currently as a visiting scholar (remotely) at UCLA’s Department of Biostatistics.
Martijn is the creator of White Rabbit, Rabbit in a Hat, and Usagi, and co-led the creation of The Book of OHDSI. Today, he leads three OHDSI workgroups: Health Analytics Data-to-Evidence Suite (HADES), Methods Research, and Generative AI and Analytics. His research focuses on developing and evaluating methods for observational studies, with a particular interest in using negative controls to assess study validity, and recently the application of generative AI and foundational models.
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.
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.
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.
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.
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.
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, 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.
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.
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.
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.
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.
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.
Steele E, Tucker A, t Hoen PA, Schuemie MJ. Literature-based priors for gene regulatory networks. Bioinformatics 2009; 25: 1768-1774.
Schuemie MJ, Kors JA. Jane: suggesting journals, finding experts. Bioinformatics 2008; 24: 727-728.
Schuemie MJ, van der Straaten P, Krijn M, van der Mast CA. Research on presence in virtual reality: a survey. Cyberpsychol Behav 2001; 4: 183-201.
