Abstract
There is growing interest in leveraging real-world data to complement knowledge gained from randomized clinical trials and inform the design of prospective randomized studies in oncology. The present study compared clinical outcomes in women with metastatic breast cancer who received letrozole as first-line monotherapy in oncology practices across the United States versus patients in the letrozole-alone cohort of the PALOMA-2 phase 3 trial. The real-world cohort (N = 107) was derived from de-identified patient data from the Flatiron Health electronic health record database. The clinical trial cohort (N = 222) comprised postmenopausal women in the letrozole-alone arm of PALOMA-2. Patients in the real-world cohort received letrozole monotherapy per labeling and clinical judgment; patients in PALOMA-2 received letrozole 2.5 mg/d, continuous. Real-world survival and response rates were based on evidence of disease burden curated from clinician notes, radiologic reports, and pathology reports available in the electronic health record. Progression-free survival and objective response rate in PALOMA-2 were based on Response Evaluation Criteria in Solid Tumors v1.1. Concordance of survival and response rates were retrospectively assessed using inverse probability of treatment weighting-adjusted Cox regression analysis. Inverse probability of treatment weighting-adjusted Cox regression results showed similar median progression-free survival in the real-world and PALOMA-2 cohorts (18.4 and 16.6 months, respectively): the hazard ratio using real-world data as reference was 1.04 (95% CI, 0.69–1.56). No significant difference was observed in response rates: 41.8% in the real-world cohort vs 39.4% in the PALOMA-2 cohort (odds ratio using real-world data as reference: 0.91 [95% CI, 0.57–1.44]). These findings indicate that data abstracted from electronic health records with proper quality controls can yield meaningful information on clinical outcomes. These data increase confidence in the use of real-world assessments of progression and response as efficacy endpoints.Trial registration NCT01740427; Funding: Pfizer.
Highlights
These findings indicate that data abstracted from electronic health records with proper quality controls can yield meaningful information on clinical outcomes
Real-world evidence is generated from real-world data documented during the course of routine clinical care. [2, 6,7,8] Real-world data can be derived from a range of sources, including electronic health records (EHRs), patient/disease registries, mobile devices and applications, genomic datasets, and medical/pharmacy claims databases. [2,3,4, 7, 8] these resources contain a wealth of information, they are designed to support clinical care and practice management, not clinical research
To evaluate the relationship between real-world and clinical trial outcomes in oncology, it is critical to assess the comparability of the data derived in each of these settings while minimizing the effect of confounding due to differences in prognostically important variables. [11, 12] The primary objective of this study was to compare progression free survival (PFS) and response rates generated using real-world data reflecting routine clinical care with outcomes observed in a traditional randomized clinical trials (RCTs)
Summary
Real-world evidence is playing an increasingly important role in regulatory decision-making, drug development, and clinical practice. [1,2,3,4] Because less than 5% of cancer patients participate in randomized clinical trials, [5] real-world evidence can provide valuable information on disease course and treatment outcomes of patients receiving care in front-line routine clinical settings, as well as insights on the generalizability of clinical trial findings to real-world patient populations. [3, 4, 6]Real-world evidence is generated from real-world data documented during the course of routine clinical care. [2, 6,7,8] Real-world data can be derived from a range of sources, including electronic health records (EHRs), patient/disease registries, mobile devices and applications, genomic datasets, and medical/pharmacy claims databases. [2,3,4, 7, 8] these resources contain a wealth of information, they are designed to support clinical care and practice management, not clinical research. [4] Unlike randomized clinical trials (RCTs), which limit variability and ensure the quality of data collected through strict protocols and standardized methods such as case report forms, real-world datasets are typically disorganized and unstructured, requiring complex curation in order to be useful for research analyses. The quality and consistency of data in real-world sources, such as EHRs, can vary widely depending on the data curation processes used as well as on clinician-, practice-, and patient-related factors These discrepancies can make it difficult to compare data collected in real-world settings with those from controlled clinical trials. [11, 12] The primary objective of this study was to compare PFS and response rates generated using real-world data reflecting routine clinical care with outcomes observed in a traditional RCT To achieve this goal, we analyzed data from a curated EHR-derived real-world dataset to compare outcomes in a cohort of women with hormone-receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2–) metastatic breast cancer (mBC) who received first-line letrozole therapy in a real-world setting with those in the control arm of the phase 3 PALOMA-2 trial. We analyzed data from a curated EHR-derived real-world dataset to compare outcomes in a cohort of women with hormone-receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2–) metastatic breast cancer (mBC) who received first-line letrozole therapy in a real-world setting with those in the control arm of the phase 3 PALOMA-2 trial. [13] An inverse probability of treatment weighting (IPTW) approach was used to account for potential baseline differences in the real-world and PALOMA-2 cohorts, which allowed retrospective evaluation of the comparability of the realworld and traditional RECIST-based clinical trial endpoints in 2 similar cohorts. [14,15,16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
