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Interim recruitment prediction for multi-center clinical trials.

Szymon Urbas1, Chris Sherlock2, Paul Metcalfe3

  • 1STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK.

Biostatistics (Oxford, England)
|September 26, 2020
PubMed
Summary

This study presents a new framework for predicting clinical trial recruitment, addressing limitations of current models. The developed methodology improves prediction accuracy for multi-center trials, especially in oncology.

Keywords:
Bayesian prediction modelingPoisson-gamma modelclinical trial recruitmentinhomogeneous Poisson processmodel averaging

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Area of Science:

  • Clinical trial operations
  • Statistical modeling
  • Biostatistics

Background:

  • Existing time-homogeneous models for multi-center clinical trial recruitment often yield overly optimistic and narrow prediction intervals.
  • There is a need for more accurate and robust methods to monitor, model, and predict patient recruitment in complex trial settings.

Purpose of the Study:

  • To introduce a general framework for monitoring, modeling, and predicting patient recruitment in multi-center clinical trials.
  • To address the limitations of existing models by accounting for decaying recruitment rates and model uncertainty.

Main Methods:

  • Development of two tests for detecting decay in recruitment rates.
  • Introduction of an inhomogeneous Poisson process model with a monotonically decaying intensity, adaptable to various parametric curve-shapes.
  • Utilizing Bayesian model averaging for predictions, incorporating parameter and model uncertainty.

Main Results:

  • The proposed framework demonstrates improved prediction accuracy compared to existing methods in simulated and real oncology trial data.
  • The methodology effectively identifies unexpected changes and patterns in patient accrual.
  • The tests for recruitment rate decay show reliable performance in power studies.

Conclusions:

  • The new framework provides a more realistic and robust approach to predicting clinical trial recruitment, particularly for multi-center studies.
  • The methodology enhances the ability to monitor trial progress and detect deviations from expected recruitment patterns.
  • This work offers valuable tools for optimizing clinical trial planning and management.