Gabrielle B Rocque1, Nicole E Caston2, Jeffrey A Franks2, Courtney P Williams2, Monica S Aswani3, Andres Azuero4, Risha Gidwani5. 1. Division of Hematology and Oncology, University of Alabama at Birmingham (UAB), WTI 240E, Birmingham, AL, 35294, USA. grocque@uabmc.edu. 2. Division of Hematology and Oncology, University of Alabama at Birmingham (UAB), WTI 240E, Birmingham, AL, 35294, USA. 3. UAB School of Health Professions, Birmingham, AL, USA. 4. UAB School of Nursing, Birmingham, AL, USA. 5. Department of Health Management & Policy, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
Abstract
PURPOSE: The extent to which evidence-based treatments are applied to populations not well represented in early stage breast cancer (EBC) trials remains unknown. This study evaluated treatment intensity for patients traditionally well represented, underrepresented, and unrepresented in clinical trials. METHODS: This retrospective cohort study used real-world data to evaluate the intensity (high or low) of EBC chemotherapy by patient characteristics (age, race and ethnicity, presence of comorbidity) denoting clinical trial representation status (well represented, underrepresented, unrepresented) for patients diagnosed from 2011 to 2020. Odds ratios (OR) from a logistic regression model was used to evaluate the association between receipt of high-intensity chemotherapy and clinical trial representation status characteristics adjusting for cancer stage and subtype. RESULTS: Of 970 patients with EBC, 41% were characterized as well represented, 45% as underrepresented, and 13% as unrepresented in clinical trials. In adjusted models, patients aged ≥ 70 versus 45-69 had lower odds of receiving a high-intensity treatment (OR 0.40, 95% CI 0.26-0.60), while those aged < 45 versus 45-69 had higher odds of receiving high-intensity treatment (OR 1.82, 95% CI 1.10-3.01). In predicted estimates, the proportion of patients receiving a high-intensity treatment was 87% for patients aged < 45, 79% for patients aged 45-69, and 60% for patients aged ≥ 70. CONCLUSION: 59% of the EBC population is not well represented in clinical trials. Age was associated with differential treatment intensity. Widening clinical trial eligibility criteria should be considered to better understand survival outcomes, toxicity effects, and ultimately make evidence-based treatment decisions using a more diverse sample.
PURPOSE: The extent to which evidence-based treatments are applied to populations not well represented in early stage breast cancer (EBC) trials remains unknown. This study evaluated treatment intensity for patients traditionally well represented, underrepresented, and unrepresented in clinical trials. METHODS: This retrospective cohort study used real-world data to evaluate the intensity (high or low) of EBC chemotherapy by patient characteristics (age, race and ethnicity, presence of comorbidity) denoting clinical trial representation status (well represented, underrepresented, unrepresented) for patients diagnosed from 2011 to 2020. Odds ratios (OR) from a logistic regression model was used to evaluate the association between receipt of high-intensity chemotherapy and clinical trial representation status characteristics adjusting for cancer stage and subtype. RESULTS: Of 970 patients with EBC, 41% were characterized as well represented, 45% as underrepresented, and 13% as unrepresented in clinical trials. In adjusted models, patients aged ≥ 70 versus 45-69 had lower odds of receiving a high-intensity treatment (OR 0.40, 95% CI 0.26-0.60), while those aged < 45 versus 45-69 had higher odds of receiving high-intensity treatment (OR 1.82, 95% CI 1.10-3.01). In predicted estimates, the proportion of patients receiving a high-intensity treatment was 87% for patients aged < 45, 79% for patients aged 45-69, and 60% for patients aged ≥ 70. CONCLUSION: 59% of the EBC population is not well represented in clinical trials. Age was associated with differential treatment intensity. Widening clinical trial eligibility criteria should be considered to better understand survival outcomes, toxicity effects, and ultimately make evidence-based treatment decisions using a more diverse sample.
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