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Tempus Announces Six Posters Accepted for Presentation at ISPOR 2025
CHICAGO--(BUSINESS WIRE)-- Tempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine and patient care, has
About this update from Tempus Ai, Inc.
[{"type":"text","content":" CHICAGO--(BUSINESS WIRE)--\nTempus AI, Inc. (NASDAQ: TEM), a technology company leading the adoption of AI to advance precision medicine and patient care, has announced the presentation of six posters, including one oral presentation, at the 2025 annual meeting of the Professional Society for Health Economics and Outcomes Research (ISPOR), taking place May 13-16 in Montreal, Canada. Tempus researchers are showcasing scientific and clinical studies highlighting the impact of AI and real-world data on health economics and outcomes research.\n\n“The research we’re presenting at ISPOR 2025 underscores the powerful potential of integrating clinical, molecular, and claims data to unlock actionable insights that drive more personalized and effective cancer care,” said Emilie Scherrer, Senior Director and Head of Outcomes Research, at Tempus. “At Tempus, we share ISPOR’s deep focus on empowering providers and health systems with the real-world data they need to optimize treatment strategies and improve outcomes for their patients.”\n\nResearch highlights include:\n\n\nOral Presentation: Oncology Trial Emulation Using Real-World Electronic Health Record Data: Results of the Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation (CARE) Initiative\n\n\nDate/Time: Thursday, May 15; 10:15 AM - 11:15 AM ET\n\n\nOverview: The Coalition to Advance Real-World Evidence through Randomized Controlled Trial (RCT) Emulation (CARE) Initiative seeks to advance understanding of when real-world data (RWD) can generate valid treatment effectiveness estimates by emulating RCTs. This study presents findings from three oncology emulations. The KEYNOTE-189 (metastatic NSCLC) and PALOMA-2 (advanced breast cancer) trials were emulated using electronic health record datasets. Trial entry criteria were applied, and treatment status was based on first-line medications. Inverse probability of treatment weighting controlled for baseline confounding, and Kaplan-Meier and Cox models estimated primary outcomes. In the KEYNOTE-189 emulation, the real-world progression-free survival (rwPFS) hazard ratio (HR) in one dataset was similar to the RCT finding, while the other was closer to the null. PALOMA-2's rwPFS HR was also closer to the null. Real-world overall survival estimates in KEYNOTE-189 also varied across datasets. The researchers ...