{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T08:40:27Z","timestamp":1792658427998,"version":"4.5.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The potential of \u201cbig data\u201d in health research remains largely untapped, particularly concerning real-world data sources such as administrative health data and electronic medical records. While healthcare insurance claims data have been essential for assessing medication safety and effectiveness within indicated patient populations, exploring broader drug-outcome associations could uncover significant insights.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>The REWARD (REal-World Evidence and Research of Drug performance) framework was established to perform large-scale analytics on medication benefits beyond their original indications. Employing an \u201call-by-all\u201d approach, it investigates all medication-outcome pairs using standardized vocabularies from the OMOP common data model and implements causal inference methods, including self-controlled cohort and active comparator new-user designs. Negative control calibration and large-scale propensity scores are used to control for systematic bias in the study designs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our framework enables the identification of new benefits associated with thousands of medications across millions of patients. In particular, REWARD facilitates insights into disease mechanisms to guide the development of novel interventions, identifies opportunities for drug repurposing, and informs potential additional indications for drugs in clinical development. REWARD has led to various publications with the goal of informing new drug development.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>REWARD is distinguished by its open-source implementation, use of standardized OMOP CDM vocabularies, and integration of best-practice pharmacoepidemiologic methods. Results are best interpreted as hypothesis-generating signals, with the active comparator new-user design providing higher causal rigor for prioritized drug-outcome pairs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The REWARD framework demonstrates how real-world evidence can be harnessed to address unmet medical needs, particularly for diseases lacking effective approved treatments. By making the REWARD analytic package open-source and accessible, we promote an open scientific approach, while maintaining best practices in pharmacoepidemiology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag137","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:04:38Z","timestamp":1784721878000},"page":"2102-2113","source":"Crossref","is-referenced-by-count":0,"title":["REWARD\u2014an open-source framework for identifying the unknown benefits of existing medications to inform drug discovery, development, and repurposing"],"prefix":"10.1093","volume":"33","author":[{"given":"David M","family":"Kern","sequence":"first","affiliation":[{"name":"Global Epidemiology, Johnson & Johnson , Horsham, PA 19044,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Bohn","sequence":"additional","affiliation":[{"name":"Global Epidemiology, Johnson & Johnson , Titusville, NJ 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