{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T03:55:15Z","timestamp":1773028515410,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013120","name":"European Medicines Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013120","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010767","name":"Innovative Medicines Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010767","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>In real-world data (RWD), defining the observation period\u2014the time during which a patient is considered observable\u2014is critical for estimating incidence rates (IRs) and other outcomes. Yet, in the absence of explicit enrollment information, this period must often be inferred, introducing potential bias.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This study evaluates methods for defining observation periods and their impact on IR estimates across multiple database types. We applied 3 methods for defining observation periods: (1) a persistence + surveillance window approach, (2) an age- and gender-adjusted method based on time between healthcare events, and (3) the min\/max method. These were tested across 11 RWD databases, including both enrollment-based and encounter-based sources. Enrollment time was used as the reference standard in eligible databases. To assess the impact on epidemiologic results, we replicated a prior study of adverse event incidence, comparing IRs and calculating mean squared error between methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Incidence rates decreased as observation periods lengthened, driven by increases in the person-time denominator. The persistence + surveillance method produced estimates closest to enrollment-based rates when appropriately balanced. The min\/max approach yielded inconsistent results, particularly in encounter-based databases, with greater error observed in databases with longer time spans.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>These findings suggest that assumptions about data completeness and population observability significantly affect incidence estimates. Observation period definitions substantially influence outcome measurement in RWD studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Standardized, transparent approaches are necessary to ensure valid, reproducible results\u2014especially in databases lacking defined enrollment.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf119","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T07:33:07Z","timestamp":1751959987000},"page":"1434-1444","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation of the impact of defining observable time in real-world data on outcome incidence"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2570-2124","authenticated-orcid":false,"given":"Clair","family":"Blacketer","sequence":"first","affiliation":[{"name":"Coordinating Center, Observational Health Data Sciences and Informatics (OHDSI) , New York, NY, 10032,","place":["United States"]},{"name":"Department of Medical Informatics, Erasmus University Medical Center , Rotterdam, NL, 3015 GD,","place":["United States"]},{"name":"Johnson & Johnson , Raritan, NJ, 08869,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank J","family":"DeFalco","sequence":"additional","affiliation":[{"name":"Coordinating Center, Observational Health Data Sciences and Informatics (OHDSI) , New York, NY, 10032,","place":["United States"]},{"name":"Johnson & Johnson , Raritan, NJ, 08869,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5682-3688","authenticated-orcid":false,"given":"Mitchell M","family":"Conover","sequence":"additional","affiliation":[{"name":"Coordinating Center, Observational Health Data Sciences and Informatics (OHDSI) , New York, NY, 10032,","place":["United 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