What is it about?
This article is devoted to developing further a statistical technique for modeling patient recruitment together with randomization process in multicentre clinical trials. The analytic technique for predicting the number of patients recruited in different centers/regions for ongoing trials accounting for possible delays and closure of some centers is developed. The asymptotic properties of the recruitment in particular regions are investigated and the analysis of recruitment performance in centers/regions is provided. The approximations and predictive confidence bounds for the number of randomized patients in some region using center-stratified randomization are derived. These results are used for creating software tools for predictive patient recruitment and drug supply modeling in GlaxoSmithKline.
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Why is it important?
At trial design and ongoing stage it is crucial to predict how many patients will be recruited in particular regions and randomized to particular treatments as this may affect the power of statistical tests and the predicted amount of drug supply required to satisfy patient demand.
Perspectives
The developed models and statistical techniques have various opportunities to be expanded for predicting patient recruitment under different restrictions, e.g. having caps on centres/country levels, forecasting recruitment performance and risk-based monitoring the different recruitment characteristics, e.g. low/high recruiting centres, countries, etc.
Prof Vladimir Anisimov
Amgen Inc
Read the Original
This page is a summary of: Statistical Modeling of Clinical Trials (Recruitment and Randomization), Communication in Statistics- Theory and Methods, October 2011, Taylor & Francis,
DOI: 10.1080/03610926.2011.581189.
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