Cargando…

Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials

BACKGROUND/AIMS: Determining whether clinical trial findings are applicable to diverse, real-world patient populations can be challenging when the full demographic characteristics of enrolled patients are not consistently reported. Here, we present the results of a descriptive analysis of racial and...

Descripción completa

Detalles Bibliográficos
Autores principales: Kuri, Lorena, Setru, Sagar, Liu, Gengyuan, Reed, Diane Moniz, Weigand, David, Surampudi, Aparna, Berger, Susan, Paulucci, David, Rai, Angshu, Sethuraman, Venkat, Vito, Blythe, Kellar-Wood, Helen, Balan, Mariann Micsinai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638849/
https://www.ncbi.nlm.nih.gov/pubmed/37309819
http://dx.doi.org/10.1177/17407745231180506
_version_ 1785146605702217728
author Kuri, Lorena
Setru, Sagar
Liu, Gengyuan
Reed, Diane Moniz
Weigand, David
Surampudi, Aparna
Berger, Susan
Paulucci, David
Rai, Angshu
Sethuraman, Venkat
Vito, Blythe
Kellar-Wood, Helen
Balan, Mariann Micsinai
author_facet Kuri, Lorena
Setru, Sagar
Liu, Gengyuan
Reed, Diane Moniz
Weigand, David
Surampudi, Aparna
Berger, Susan
Paulucci, David
Rai, Angshu
Sethuraman, Venkat
Vito, Blythe
Kellar-Wood, Helen
Balan, Mariann Micsinai
author_sort Kuri, Lorena
collection PubMed
description BACKGROUND/AIMS: Determining whether clinical trial findings are applicable to diverse, real-world patient populations can be challenging when the full demographic characteristics of enrolled patients are not consistently reported. Here, we present the results of a descriptive analysis of racial and ethnic demographic information for patients in Bristol Myers Squibb (BMS)–sponsored oncology trials in the United States (US) and describe factors associated with increased patient diversity. METHODS: BMS–sponsored oncology trials conducted at US sites with study enrollment dates between 1 January 2013 and 31 May 2021 were analyzed. Patient race/ethnicity information was self-reported in case report forms. As principal investigators (PIs) did not report their own race/ethnicity, a deep-learning algorithm (ethnicolr) was used to predict PI race/ethnicity. Trial sites were linked to counties to understand the role of county-level demographics. The impact of working with patient advocacy and community-based organizations to increase diversity in prostate cancer trials was analyzed. The magnitude of associations between patient diversity and PI diversity, US county demographics, and recruitment interventions in prostate cancer trials were assessed by bootstrapping. RESULTS: A total of 108 trials for solid tumors were analyzed, including 15,763 patients with race/ethnicity information and 834 unique PIs. Of the 15,763 patients, 13,968 (89%) self-reported as White, 956 (6%) Black, 466 (3%) Asian, and 373 (2%) Hispanic. Among 834 PIs, 607 (73%) were predicted to be White, 17 (2%) Black, 161 (19%) Asian, and 49 (6%) Hispanic. A positive concordance was observed between Hispanic patients and PIs (mean = 5.9%; 95% confidence interval (CI) = 2.4, 8.9), a less positive concordance between Black patients and PIs (mean = 1.0%; 95% CI = −2.7, 5.5), and no concordance between Asian patients and PIs. Geographic analyses showed that more non-White patients enrolled in study sites in counties with higher proportions of non-White residents (e.g. a county population that was 5%–30% Black had 7%–14% more Black patients enrolled in study sites). Following purposeful recruitment efforts in prostate cancer trials, 11% (95% CI = 7.7, 15.3) more Black men enrolled in prostate cancer trials. CONCLUSION: Most patients in these clinical trials were White. PI diversity, geographic diversity, and recruitment efforts were related to greater patient diversity. This report constitutes an essential step in benchmarking patient diversity in BMS US oncology trials and enables BMS to understand which initiatives may increase patient diversity. While complete reporting of patient characteristics such as race/ethnicity is critical, identifying diversity improvement tactics with the highest impact is essential. Strategies with the greatest concordance to clinical trial patient diversity should be implemented to make meaningful improvements to the diversity of clinical trial populations.
format Online
Article
Text
id pubmed-10638849
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-106388492023-11-14 Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials Kuri, Lorena Setru, Sagar Liu, Gengyuan Reed, Diane Moniz Weigand, David Surampudi, Aparna Berger, Susan Paulucci, David Rai, Angshu Sethuraman, Venkat Vito, Blythe Kellar-Wood, Helen Balan, Mariann Micsinai Clin Trials Articles BACKGROUND/AIMS: Determining whether clinical trial findings are applicable to diverse, real-world patient populations can be challenging when the full demographic characteristics of enrolled patients are not consistently reported. Here, we present the results of a descriptive analysis of racial and ethnic demographic information for patients in Bristol Myers Squibb (BMS)–sponsored oncology trials in the United States (US) and describe factors associated with increased patient diversity. METHODS: BMS–sponsored oncology trials conducted at US sites with study enrollment dates between 1 January 2013 and 31 May 2021 were analyzed. Patient race/ethnicity information was self-reported in case report forms. As principal investigators (PIs) did not report their own race/ethnicity, a deep-learning algorithm (ethnicolr) was used to predict PI race/ethnicity. Trial sites were linked to counties to understand the role of county-level demographics. The impact of working with patient advocacy and community-based organizations to increase diversity in prostate cancer trials was analyzed. The magnitude of associations between patient diversity and PI diversity, US county demographics, and recruitment interventions in prostate cancer trials were assessed by bootstrapping. RESULTS: A total of 108 trials for solid tumors were analyzed, including 15,763 patients with race/ethnicity information and 834 unique PIs. Of the 15,763 patients, 13,968 (89%) self-reported as White, 956 (6%) Black, 466 (3%) Asian, and 373 (2%) Hispanic. Among 834 PIs, 607 (73%) were predicted to be White, 17 (2%) Black, 161 (19%) Asian, and 49 (6%) Hispanic. A positive concordance was observed between Hispanic patients and PIs (mean = 5.9%; 95% confidence interval (CI) = 2.4, 8.9), a less positive concordance between Black patients and PIs (mean = 1.0%; 95% CI = −2.7, 5.5), and no concordance between Asian patients and PIs. Geographic analyses showed that more non-White patients enrolled in study sites in counties with higher proportions of non-White residents (e.g. a county population that was 5%–30% Black had 7%–14% more Black patients enrolled in study sites). Following purposeful recruitment efforts in prostate cancer trials, 11% (95% CI = 7.7, 15.3) more Black men enrolled in prostate cancer trials. CONCLUSION: Most patients in these clinical trials were White. PI diversity, geographic diversity, and recruitment efforts were related to greater patient diversity. This report constitutes an essential step in benchmarking patient diversity in BMS US oncology trials and enables BMS to understand which initiatives may increase patient diversity. While complete reporting of patient characteristics such as race/ethnicity is critical, identifying diversity improvement tactics with the highest impact is essential. Strategies with the greatest concordance to clinical trial patient diversity should be implemented to make meaningful improvements to the diversity of clinical trial populations. SAGE Publications 2023-06-13 2023-12 /pmc/articles/PMC10638849/ /pubmed/37309819 http://dx.doi.org/10.1177/17407745231180506 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Articles
Kuri, Lorena
Setru, Sagar
Liu, Gengyuan
Reed, Diane Moniz
Weigand, David
Surampudi, Aparna
Berger, Susan
Paulucci, David
Rai, Angshu
Sethuraman, Venkat
Vito, Blythe
Kellar-Wood, Helen
Balan, Mariann Micsinai
Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title_full Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title_fullStr Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title_full_unstemmed Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title_short Data-driven strategies for increasing patient diversity in Bristol Myers Squibb–sponsored US oncology clinical trials
title_sort data-driven strategies for increasing patient diversity in bristol myers squibb–sponsored us oncology clinical trials
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638849/
https://www.ncbi.nlm.nih.gov/pubmed/37309819
http://dx.doi.org/10.1177/17407745231180506
work_keys_str_mv AT kurilorena datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT setrusagar datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT liugengyuan datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT reeddianemoniz datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT weiganddavid datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT surampudiaparna datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT bergersusan datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT pauluccidavid datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT raiangshu datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT sethuramanvenkat datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT vitoblythe datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT kellarwoodhelen datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials
AT balanmariannmicsinai datadrivenstrategiesforincreasingpatientdiversityinbristolmyerssquibbsponsoredusoncologyclinicaltrials