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Background <p>There has been a recent push for biomedical research to incorporate more demographically, ethnically, and medically diverse cohorts – individuals whom the NIH designates as “underrepresented in biomedical research” (UBR). In clinical trials, researchers often set out to achieve target rates of UBR, yet there are no methods used to help achieve these targets. Prediction and monitoring of the rate of UBR (rUBR) – the proportion of designated UBR participants - is essential to ensure these targets are met.</p> Methods <p>We propose a monitoring tool that can simultaneously predict overall accrual and rUBR. The rUBR predictive algorithm extends the Bayesian overall accrual model by adding a Bayesian beta-binomial model.</p> Results <p>We apply our method to two real-world completed clinical trial datasets: ADORE (An Assessment of DHA On Reducing Early preterm birth) and Quit2Live - a clinical trial to examine disparities in quitting between African American and White adult smokers. By doing this, we show the usefulness of this method at various time points in these trials and demonstrate that it can be used to monitor overall accrual and the rUBR for future research, including the All of Us Research Program.</p> Conclusion <p>The simultaneous prediction of both overall accrual and rUBR gained by this method adds a novel probabilistic method to allow researchers to improve the monitoring process of their trial.</p>

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Prediction and monitoring of accrual and rate of underrepresented biomedical research group using bayesian methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月19日

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  • Kaustubh S. Nimkar,
  • Byron J. Gajewski,
  • Dinesh Pal Mudaranthakam,
  • Jeffery A. Thompson,
  • Miranda E. Handke,
  • Robert N. Montgomery,
  • Akinlolu O. Ojo

摘要

Background

There has been a recent push for biomedical research to incorporate more demographically, ethnically, and medically diverse cohorts – individuals whom the NIH designates as “underrepresented in biomedical research” (UBR). In clinical trials, researchers often set out to achieve target rates of UBR, yet there are no methods used to help achieve these targets. Prediction and monitoring of the rate of UBR (rUBR) – the proportion of designated UBR participants - is essential to ensure these targets are met.

Methods

We propose a monitoring tool that can simultaneously predict overall accrual and rUBR. The rUBR predictive algorithm extends the Bayesian overall accrual model by adding a Bayesian beta-binomial model.

Results

We apply our method to two real-world completed clinical trial datasets: ADORE (An Assessment of DHA On Reducing Early preterm birth) and Quit2Live - a clinical trial to examine disparities in quitting between African American and White adult smokers. By doing this, we show the usefulness of this method at various time points in these trials and demonstrate that it can be used to monitor overall accrual and the rUBR for future research, including the All of Us Research Program.

Conclusion

The simultaneous prediction of both overall accrual and rUBR gained by this method adds a novel probabilistic method to allow researchers to improve the monitoring process of their trial.