BioMetrics.com

When to stop collecting data

James Roger and Mike Kenward

Adaptive optimal design for the Emax model and its application in clinical trials

Sergei Leonov & Sam Miller

Adaptive optimal design for the Emax model and its application in clinical trials

Sergei Leonov and Sam Miller

Fedorov V, Mannino F, Zhang R (2009). Consequences of dichotomization. Pharmaceutical Statistics 8:50–61.

Statistics in Pharmaceutical Development and Manufacturing

John J. Peterson, Ronald D. Snee, Paul R. McAllister, Timothy L. Schofield, and Anthony Carella

Abstract: The pharmaceutical industry is undergoing rapid change and facing numerous challenges, including the demands of global competition, the need to speed up the drug development process, and the Food and Drug Administration’s (FDA’s) expectations for the incorporation of the principles of quality by design (QbD) and process analytical technology (PAT) in process and analytical development. Statistical thinking and methods play a significant role in addressing these issues. This article provides an overview of the use of statistical thinking and methods in the R&D and manufacturing functions of the pharmaceutical industry. The exposition includes the history of pharmaceutical quality and regulation, phases of pharmaceutical development and manufacturing and the basic quality and statistical tools employed in each, emerging statistical methods, the impact of statistical software and information technology, and the role of statisticians in pharmaceutical development and manufacturing. Four case studies are included to illustrate how these issues play out in actuality. A summary provides a succinct synopsis of those issues and concludes that the complex, technical nature of pharmaceutical development and manufacturing offers many opportunities for the effective use of statistical thinking and methods and that those who use these methods can become catalysts for both process development understanding and product quality improvement.

Modelling and predicting patient recruitment to ensure successful completion

Vladimir Anisimov

Abstract: Patient recruitment is an essential stage of drug development process and well recognised bottleneck of the new drug development. Existing techniques of recruitment planning are mainly deterministic and do not account for: (a) the uncertainties in input information; (b) variation in recruitment across centres; (c) stochastic fluctuations of recruitment over time. A large proportion of trials fail to recruit in time. Therefore, it is imperative to develop statistical modelling approaches that can account for various uncertainties and provide more accurate prediction.

In the talk, an advanced statistical methodology for modelling and predicting patient recruitment is discussed. It allows: 1) predicting recruitment at the initial (design) stage; 2) evaluating study performance and site productivity; 3) predicting recruitment at any interim time using data-driven approach; 4) evaluate the number of clinical centres needed to complete the recruitment in time with a given confidence and provide an adaptive adjustment of recruitment.
The results are illustrated on case studies.