ISPOR 2025
Tuesday, May 13 - Friday, May 16
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
Author(s)
Natalie Levy, PhD1, Paige Sheridan, PhD1, Ulka Campbell, PhD1, David Lenis, PhD1, Inish O'Doherty, PhD1, Adina Estrin, MPH1, Nileesa Gautam, MPH1, Thomas Zhen, BS1, Andrew Belli, MPH2, Gilis Carrigan, PhD3, Arnold Chan, ScD4, James Chen, MD5, Victoria Chia, PhD3, Neil Dhopeshwarkar, PhD6, Joy Catherine Eckert, MPH7, laura Fernandes, PhD2, Joel Greshock, PhD8, Rachele Hendricks-Sturrup, MA, MSc9, Jenny Huang, ScD10, Xiaolong Jiao, MS, MD11, Sajan Khosla, MSc12, Orsoyla Lunacsek, PhD13, Lynn McRoy, MD11, Yanina Natanzon, MS, PhD14, Osayi Obviosa, PhD15, Nelson D Pace, PhD15, Simone Pinheiro, ScD16, Megan Rees, PharmD17, Jennifer Rider, ScD18, Mothaffar Rimawi, MD19, Travis Robinson, MBA17, Carla Rodriguez-Watson, PhD7, Chithra Sangli, MA, MSc5, Khaled Sarsour, PhD20, Sebastian Schneeweiss, ScD, MD21, Mark A. Shapiro, MA, MBA22, Mark Stewart, PhD23, Aliki Taylor, PhD24, Ck Wang, MD2, Shirley Wang, PhD21, Yiduo Zhang, BA, MA, PhD25, Ann M. madsen, phd1.
1Aetion, Inc, New York, NY, USA, 2Cota, Inc, New York, NY, USA, 3Amgen, Thousand Oaks, CA, USA, 4TriNetX, Cambridge, MA, USA, 5Tempus, Chicago, IL, USA, 6TriNetX, cambridge, MA, USA, 7Reagan-Udall Foundation, Washington DC, DC, USA, 8Johnson & Johnson, Cambridge, MA, USA, 9Duke-Margolis Institute for Health Policy, Washington DC, DC, USA, 10Gilead, Inc, Foster City, CA, USA, 11Pfizer, Inc, New York, NY, USA, 12AstraZeneca, Cambridge, United Kingdom, 13Bayer, Whippany, NJ, USA, 14Concert AI, San Francisco, CA, USA, 15Abbvie, North Chicago, IL, USA, 16AbbVie, North Chicago, IL, USA, 17Loopback Analytics, Dallas, TX, USA, 18Concert AI, Cambridge, MA, USA, 19Baylor College of Medicine, Houston, TX, USA, 20Johnson & Johnson, San Francisco, CA, USA, 21Harvard Medical School, Boston, MA, USA, 22Xcures, Durhan, NC, USA, 23Friends of Cancer Research, Washington DC, DC, USA, 24Gilead, Foster City, CA, USA, 25AstraZeneca, Gaithersburg, MD, USA.
1Aetion, Inc, New York, NY, USA, 2Cota, Inc, New York, NY, USA, 3Amgen, Thousand Oaks, CA, USA, 4TriNetX, Cambridge, MA, USA, 5Tempus, Chicago, IL, USA, 6TriNetX, cambridge, MA, USA, 7Reagan-Udall Foundation, Washington DC, DC, USA, 8Johnson & Johnson, Cambridge, MA, USA, 9Duke-Margolis Institute for Health Policy, Washington DC, DC, USA, 10Gilead, Inc, Foster City, CA, USA, 11Pfizer, Inc, New York, NY, USA, 12AstraZeneca, Cambridge, United Kingdom, 13Bayer, Whippany, NJ, USA, 14Concert AI, San Francisco, CA, USA, 15Abbvie, North Chicago, IL, USA, 16AbbVie, North Chicago, IL, USA, 17Loopback Analytics, Dallas, TX, USA, 18Concert AI, Cambridge, MA, USA, 19Baylor College of Medicine, Houston, TX, USA, 20Johnson & Johnson, San Francisco, CA, USA, 21Harvard Medical School, Boston, MA, USA, 22Xcures, Durhan, NC, USA, 23Friends of Cancer Research, Washington DC, DC, USA, 24Gilead, Foster City, CA, USA, 25AstraZeneca, Gaithersburg, MD, USA.
OBJECTIVES: The CARE Initiative seeks to advance understanding of when real-world data (RWD) can generate valid treatment effectiveness estimates by emulating randomized controlled trials (RCTs). We present findings from three oncology RCT emulations.
METHODS: Following feasibility assessments of candidate RCTs in available U.S. data sources, we emulated the KEYNOTE-189 (metastatic NSCLC) trial of first-line pembrolizumab+chemotherapy vs. chemotherapy in two electronic health record datasets (DS1 and DS2) and the PALOMA-2 (advanced breast cancer) trial of first-line palbociclib+letrozole vs. letrozole in DS1. Trial entry criteria were applied, as feasible. Treatment status was based on first-line regimens (using data partner-defined line of therapy algorithms) initiated during a fixed ascertainment period. Inverse probability of treatment weighting was used to control baseline confounding. Cox proportional hazards models were used to estimate the primary outcome(s). RWD-based estimates were assessed for qualitative agreement (same direction/magnitude) with RCT results.
RESULTS: The KEYNOTE-189 emulation real-world progression-free survival (rwPFS) hazard ratio (HR) in DS2 was of similar magnitude to the RCT finding, whereas the DS1 result did not demonstrate qualitative agreement [RCT: HR=0.52 (0.43, 0.64); DS2: HR=0.64 (0.47, 0.84); DS1: HR=0.81 (0.65, 1.00)]. KEYNOTE-189 emulation real-world overall survival estimates differed from the RCT results [RCT: 0.49 (0.38, 0.64), DS2: 0.89 (0.63, 1.29), DS1: 1.18 (0.95, 1.44)]. The PALOMA-2 emulation rwPFS HR also differed from RCT findings [RCT: HR=0.58 (0.46, 0.72); DS1: HR=0.84 (0.61, 1.23)].
CONCLUSIONS: Our results highlight that RWD oncology emulation conclusions depend on dataset features (e.g., care setting, therapy uptake, data completeness), treatment modality, and real-world clinical care. Future work should emphasize fit-for-purpose RWD selection and consideration of real-world care patterns to generate robust, interpretable real-world evidence.
METHODS: Following feasibility assessments of candidate RCTs in available U.S. data sources, we emulated the KEYNOTE-189 (metastatic NSCLC) trial of first-line pembrolizumab+chemotherapy vs. chemotherapy in two electronic health record datasets (DS1 and DS2) and the PALOMA-2 (advanced breast cancer) trial of first-line palbociclib+letrozole vs. letrozole in DS1. Trial entry criteria were applied, as feasible. Treatment status was based on first-line regimens (using data partner-defined line of therapy algorithms) initiated during a fixed ascertainment period. Inverse probability of treatment weighting was used to control baseline confounding. Cox proportional hazards models were used to estimate the primary outcome(s). RWD-based estimates were assessed for qualitative agreement (same direction/magnitude) with RCT results.
RESULTS: The KEYNOTE-189 emulation real-world progression-free survival (rwPFS) hazard ratio (HR) in DS2 was of similar magnitude to the RCT finding, whereas the DS1 result did not demonstrate qualitative agreement [RCT: HR=0.52 (0.43, 0.64); DS2: HR=0.64 (0.47, 0.84); DS1: HR=0.81 (0.65, 1.00)]. KEYNOTE-189 emulation real-world overall survival estimates differed from the RCT results [RCT: 0.49 (0.38, 0.64), DS2: 0.89 (0.63, 1.29), DS1: 1.18 (0.95, 1.44)]. The PALOMA-2 emulation rwPFS HR also differed from RCT findings [RCT: HR=0.58 (0.46, 0.72); DS1: HR=0.84 (0.61, 1.23)].
CONCLUSIONS: Our results highlight that RWD oncology emulation conclusions depend on dataset features (e.g., care setting, therapy uptake, data completeness), treatment modality, and real-world clinical care. Future work should emphasize fit-for-purpose RWD selection and consideration of real-world care patterns to generate robust, interpretable real-world evidence.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
P32
Topic
Real World Data & Information Systems
Disease
SDC: Oncology