Clinical trial representativeness and treatment intensity in a real-world sample of women with early stage breast cancer

Autor: Gabrielle B. Rocque, Nicole E. Caston, Risha Gidwani, Andres Azuero, Jeffrey Franks, Monica S. Aswani, Courtney P. Williams
Rok vydání: 2021
Předmět:
Zdroj: Breast Cancer Res Treat
ISSN: 1573-7217
0167-6806
DOI: 10.1007/s10549-021-06381-7
Popis: 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/ethnicity, presence of comorbidity) denoting clinical trial representation status (well represented, underrepresented, unrepresented) for patients diagnosed from 2011–2020. 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.
Databáze: OpenAIRE
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