{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T04:01:46Z","timestamp":1791864106151,"version":"4.3.4"},"reference-count":40,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"12","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["J Clin Oncol"],"published-print":{"date-parts":[[2026,4,20]]},"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>Randomized trials provide evidence for regulatory and clinical decision making in oncology. However, trials cannot answer every important clinical question. Evidence gaps often persist in under-represented or small patient populations and for questions about head-to-head comparisons of active treatments, complex treatment strategies (including treatment sequencing and other dynamic treatment strategies), and long-term or rare outcomes. To address these questions, clinicians and researchers increasingly turn to observational (real-world) data. The target trial framework provides a systematic approach for designing observational analyses that attempt to emulate a hypothetical pragmatic trial. This process requires that investigators specify the key components of the causal question as elements of the target trial protocol: eligibility criteria, treatment strategies, assignment procedures, follow-up, outcomes, and causal contrasts. Explicitly emulating the target trial helps investigators avoid common study design flaws that can lead to immortal time and selection bias. Although the target trial framework helps to avoid such design-related biases, it cannot eliminate biases due to inherent data limitations, such as confounding and measurement error. Here we review target trial emulation to supplement evidence from randomized trials and inform regulatory and clinical decision making in oncology. Understanding the strengths and limitations of the target trial framework improves the integration of real-world evidence into modern cancer care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/jco-25-01906","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T20:59:06Z","timestamp":1770325146000},"page":"1139-1144","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":8,"title":["Target Trial Emulation for Regulatory and Clinical Decision Making in Cancer"],"prefix":"10.1200","volume":"44","author":[{"given":"Barbra A.","family":"Dickerman","sequence":"first","affiliation":[{"name":"CAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9814-2343","authenticated-orcid":true,"given":"Xabier","family":"Garc\u00eda-Alb\u00e9niz","sequence":"additional","affiliation":[{"name":"CAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA"},{"name":"RTI Health Solutions, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1619-8456","authenticated-orcid":true,"given":"Miguel A.","family":"Hern\u00e1n","sequence":"additional","affiliation":[{"name":"CAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA"},{"name":"Department of Biostatistics, Harvard T.H. 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