Phesi analysis highlights extent of cancer trial inclusion issues

By Jenni Spinner contact

- Last updated on GMT

(FatCamera/iStock via Getty Images Plus)
(FatCamera/iStock via Getty Images Plus)

Related tags: Phesi, Data management, Cancer, diversity, Patient centricity, Oncology

The latest indepth report from the AI-centric analytics provider reveals nearly half of US cancer clinical studies enroll no Latino patient participants.

Artificial intelligence-focused analytical solutions provider Phesi has offered a glimpse into the diversity, equity, and inclusion (DE&I) issues faced by US-based clinical trial teams. In its analysis of 589,295 patients enrolled in 6,372 US-recruiting trials over the past 15 years, the company found, for example:

  • About 42% of US cancer trial cohorts do not include any African-American patients
  • 48% have no Hispanic American patient

To learn more about the analysis and what it might mean for US-recruited cancer studies, Outsourcing-Pharma connected with Gen Li, Phesi president.

OSP: Could you please share why your company decided to conduct this analysis?

GL: Late last year we conducted a large analysis of more than 1.3m US patients to investigate representation and diversity in clinical development. We found that representation of Black patients was improving in overall development, but Asian, Hispanic/Latino, Native Americans, Alaska Natives, Native Hawaiians, and other Pacific Islander populations were consistently underrepresented. We wanted to look more closely into the issue of diversity so decided to carry out a new analysis specifically looking at US cancer clinical trials, as one of the most well-resourced areas of clinical development.

OSP: Please highlight some of the most notable or surprising findings (including the figures around studies with zero patients from specific underrepresented communities).

GL: Our analysis looked at 589,295 patients participating in 6,372 US-recruiting cancer clinical trials over the past 15 years. We found that 42% of US cancer trial cohorts do not include African American patients and 48% have no Hispanic American patients. These findings are particularly concerning considering that 18.7% of the US population identify as Hispanic and Latino, and 12.1% are Black or African American (from the 2020 US Census).

Given that Black people have the highest death rate and shortest survival rate for cancer of any ethnic group in the US and are at a greater risk of developing stomach, liver, and cervical cancers from the (American Cancer Society), the lack of diversity is an urgent issue.

OSP: Were there any bright spots or progress revealed by your analysis?

OSP_PhesiDisparities_GL
Gen Li, president, Phesi

GL: Despite the poor representation of minority groups at the trial level overall, there is a small percentage of trials that did very well in including minority groups. This shows that while there is still considerable work to be done to address the lack of representative recruitment, some sponsors are taking steps to close the diversity gaps. This data is of particular importance as it gives us a framework to improve racial and ethnic diversification proactively at the planning stage.

OSP: How might health and population data help turn the disparities in health research around?

GL: There is data available today – including patient records and historical trial data – that clinical development organizations can use to design inclusive trials and avoid entrenching existing health inequities. Such data ensure that diversity is considered at the earliest stages of trial planning. This includes assessing patient demographics in particular locations to ensure representative populations of desired ethnic groups can be found and recruited. This is important because there are discrepancies between the healthcare facilities that minority groups are attending and the investigator sites that are most commonly used for clinical development.

OSP: Specifically, how can companies like Phesi help trial teams improve the inclusivity of their studies?

GL: We help clients by giving them access to the technology and integrated data sets that enable them to design trials that reflect the population a therapeutic is intended for. Our AI-driven predictive analytics approach can be applied right across protocol design, site selection, and setting inclusion/exclusion criteria – to ensure diversity is built in from the start. While it is important generally speaking to have clinical trial participants aligned with the ethnic and racial composition of our society, it is even more important to that we use data to guide us in clinical trials on diseases with distinctive racial and/or ethnic characteristics.

We’re also working closely with many clients on the development of synthetic patient profiles and the creation of digital twins. By collating extensive clinical data sets – including EPRs, patient-generated data from fitness trackers or home medical equipment, disease registries, and historical and current clinical trial data – a digital twin of a patient can be created. This not only accelerates research and eliminates the need to give placebos but can help to plug the diversity gap by ensuring trial results accurately reflect patient populations.                                                                                                                 

Related news

Show more

Related products

show more

ODM and CDASH in CRF design

ODM and CDASH in CRF design

Formedix | 15-Aug-2022 | Technical / White Paper

The lesser-known Operational Data Model (ODM) standard is often overlooked as it's not required by any regulators. So, why should you be interested...

dPCR Case Study

dPCR Case Study

CellCarta | 08-Aug-2022 | Case Study

Streamline your adoptive cell therapy program with digital PCR. Our team has a unique expertise in digital and quantitative PCR to support you in ensuring...

Standardizing data with NCI controlled terminology

Standardizing data with NCI controlled terminology

Formedix | 18-Jul-2022 | Technical / White Paper

The use of consistent terminology within and between studies is critical to enabling an efficient trial process. CDISC standards such as CDASH, SDTM, SEND...

Related suppliers

Follow us

Products

View more

Webinars