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Operational Metrics for the ELAINE 2 Study Combining a Traditional Approach With a Just-in-TIME Model
Sibel Blau1, Julio Antonio Peguero2, Halle C F Moore3
1Rainier Hematology Oncology/Northwest Medical Specialties, Seattle, WA.
Purpose:
There are numerous barriers to enrollment in oncology biomarker-driven studies.
Methods:
The ELAINE 2 study (ClinicalTrials.gov identifier: NCT04432454) is an open-label phase 2 study of lasofoxifene combined with abemaciclib in patients with advanced or metastatic estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast cancer with an ESR1 mutation. ELAINE 2 opened clinical sites by using a Traditional approach, which activated a site before patient identification, and the Tempus TIME Trial network, which opened a site only after identifying an eligible patient. This manuscript presents the operational metrics comparing the Traditional and TIME Trial site data.
Results:
The study enrolled patients over 34 weeks and 16 sites (six Traditional and 10 TIME Trial) participated. Duration for full clinical trial agreement execution for Traditional sites and TIME Trial sites averaged 200.5 (range, 142-257) and 7.6 days (range, 2-14), respectively. Institutional review board approval time for Traditional sites and TIME Trial sites was 27.5 (range, 12-71) and 3.0 days (range, 1-12), respectively. Duration from study activation to first consent was 33.3 (range, 18-58) and 8.8 days (range, 1-35) for Traditional and TIME Trial sites, respectively. The first patient on study was at a TIME Trial site 115 days before a Traditional site and the first seven patients enrolled were at TIME Trial sites. Traditional sites consented 23 and enrolled 16 patients, while TIME Trial sites consented 16 and enrolled 13. The trial enrolled 29 patients in 8.5 months with the anticipated enrollment duration being 12-18 months.
Conclusion:
The TIME Trial network opened earlier and enrolled the first study patients. These results demonstrate that the Just-in-TIME model, along with a Traditional model, can improve enrollment in biomarker-driven studies.
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