Analysis of racial distribution amongst patients in phase III cancer clinical trials.

Gopishetty, Swathi; Gupta, Shruti; Kota, Vamsi; Guddati, Achuta Kumar

doi:10.1200/jco.2019.37.15_suppl.6588

Cited by 10 publications

(15 citation statements)

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“…Even when high-quality data are available, experts may disagree about interpretation and actionability applied within a clinical setting [91]. Perhaps more problematic is the bias that can arise when extrapolating findings to ethnic, racial, or age groups not well-represented in research cohorts [92,93]. Despite the growing application of genomic testing in research and precision oncology, use cases are still limited by lack of availability of codified data elements from molecular testing on tumor samples, germline DNA, and serum biomarkers, even for healthcare systems with advanced EHRs and cancer research efforts [94].…”

Section: Opportunities and Implications For Precision Oncologymentioning

confidence: 99%

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

et al. 2020

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Objectives: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area. Methods: A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research. Results: Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized. Conclusion: While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.

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Section: Opportunities and Implications For Precision Oncologymentioning

confidence: 99%

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

et al. 2020

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“…Longstanding disparities exist in enrollment of older, female, Black, and Hispanic/Latino patients to cancer clinical trials 5,6 . However, although most of the literature on cancer clinical trial disparities has focused on later‐phase trials, few studies have examined phase 1 cancer clinical trials 7 …”

Section: Introductionmentioning

confidence: 99%

Disparities in phase 1 cancer clinical trial enrollment

Perni

Moy

Nipp

2021

Cancer

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Background Phase 1 trials are increasingly important in the molecularly driven era of oncology, but few studies have examined phase 1 participation disparities. The authors of this study investigated factors associated with phase 1 versus phase 2/3 trial enrollment. Methods They authors conducted a cross‐sectional study using serial samples of patients age ≥18 years enrolling on cancer trials from October 2011 to November 2014 at an academic cancer center. They used univariable and multivariable logistic regression models to analyze sociodemographic and clinical associations with phase 1 versus phase 2/3 trial enrollment. Results Among 3103 patients enrolled in cancer trials, 2657 unique patients participated in phase 1/2/3 trials. For patients enrolled in phase 1 (n = 1401) versus phase 2/3 (n = 1256) trials, we found no significant differences by age, insurance status, marital status, and income. Overall, 1216 (93%) White, 72 (6%) Asian, and 21 (2%) Black patients enrolled on phase 1 trials, whereas 1068 (93%) White, 40 (3%) Asian, and 43 (4%) Black patients enrolled on phase 2/3 trials. Adjusting for age, sex, race, ethnicity, insurance status, marital status, income, cancer type, disease status, travel distance, and trial year, compared with White patients, Black patients had lower phase 1 enrollment (odds ratio [OR], 0.46; 95% confidence interval [CI], 0.25‐0.82), as did Hispanic/Latino (OR, 0.25; 95% CI, 0.08‐0.79) and male patients (OR, 0.77; 95% CI, 0.62‐0.94). Asian patients had higher phase 1 enrollment (OR, 1.38; 95% CI, 0.88‐2.16). Conclusions Disparities in phase 1 versus phase 2/3 cancer clinical trial enrollment underscore the urgent need for interventions addressing inequities in early‐phase trial participation. Lay Summary Phase 1 trials are of increasing importance in oncology. The authors of the study analyzed all patients enrolling on cancer clinical trials at a large academic cancer center from October 2011 to November 2014. Among the 2657 trial participants, when age, sex, race, ethnicity, insurance status, marital status, income, cancer type, disease status, travel distance, and trial year were taken into account, Black, Hispanic/Latino, and male patients were less likely to enroll on phase 1 trials versus phase 2/3 trials. These findings suggest a need for targeted interventions to improve access to and education about phase 1 trials for Black and Hispanic/Latino patients.

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“…The unfortunate reality of the lack of representation of racial and ethnic minorities in clinical trials is a major barrier to the achievement of health equity [ 1 , 2 , 3 , 4 , 5 ]. While this is of importance to all aspects of medicine, the relevance in the context of cancer stems from the fact that the cancer treatment is life-altering, and differences based on the race and ethnic characteristics of the patient have implications on survival and quality of life.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, certain cancer diagnoses are seen more frequently in some racial/ethnic subgroups, making this under-representation even more challenging [ 6 ]. Another factor that represents an important baseline characteristic of the trial population, with implications on the applicability of the trial results in the real-world setting, is patient age [ 2 ]. Moreover, the interaction of race/ethnicity and patient age is important as age patterns for cancers can be unique to certain races, making this especially relevant for clinical trials leading to drug approval or label changes.…”

Section: Introductionmentioning

confidence: 99%

Landmark Cancer Clinical Trials and Real-World Patient Populations: Examining Race and Age Reporting

Jayakrishnan¹,

Aulakh

Baksh

et al. 2021

Cancers

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Background: Concern exists that the clinical trial populations differ from respective cancer populations in terms of their age distribution affecting the generalizability of the results, especially in underrepresented minorities. We hypothesized that the clinical trials that do not report race are likely to suffer from a higher degree of age disparity. Methods: Food and Drug Administration (FDA) drug approvals from July 2007 to June 2019 were reviewed to identify oncology approvals, and trials with age details were selected. The outcomes studied were the weighted mean difference in age between the clinical trial population and real-world population for various cancers, the prevalence of race reporting and association of age and race reporting with each other. Results: Of the 261 trials, race was reported in 223 (85.4%) of the trials, while 38 trials (14.6%) had no mention of race. Race reporting improved minimally over time: 29 (85.3%) in 2007–2010 vs. 49 (80.3%) in 2011–2014 vs. 145 (85.4%) during the period 2015–2019 (p-value = 0.41). Age discrepancy between the clinical trial population and the real-world population was higher for studies that did not report race (mean difference −8.8 years (95% CI −12.6 to −5.0 years)) vs. studies that did report it (mean difference −5.1 years, (95% CI −6.4 to −3.7 years), p-value = 0.04). Conclusion: The study demonstrates that a significant number of clinical trials leading to cancer drug approvals suffer from racial and age disparity when compared to real-world populations, and that the two factors may be interrelated. We recommend continued efforts to recruit diverse populations.

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Analysis of racial distribution amongst patients in phase III cancer clinical trials.

Cited by 10 publications

References 0 publications

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

Disparities in phase 1 cancer clinical trial enrollment

Landmark Cancer Clinical Trials and Real-World Patient Populations: Examining Race and Age Reporting

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