ArticleBMC medical research methodology2026
Prediction and monitoring of accrual and rate of underrepresented biomedical research group using bayesian methods.
Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
backgroundThere 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.
methodsWe 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.
resultsWe 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.
conclusionThe 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.
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