Bayesian approaches to Phase I clinical trials: methodological and practical aspects
Seminar Room 1, Newton Institute
Statistics plays an important role in drug development, in particular in confirmatory (phase III) clinical trials, where statistically convincing evidence is a requirement for the registration of a drug. However, statistical contributions to phase I clinical trials are typically sparse. A notable exception is oncology, where statistical methods abound.
After a short review of the main approaches to phase I cancer trials, we discuss a fully adaptive model-based Bayesian approach which strikes a reasonable balance with regard to various objectives. First, proper quantification of the risk of dose-limiting toxicities (DLT) is the key to acceptable dosing recommendations during the trial, and the declaration of the maximum tolerable dose (MTD), a dose with an acceptable risk of DLT, at the end of the trial. In other words, statistically driven dosing-recommendations should be clinically meaningful. Second, the operating characteristics of the design should be acceptable. That is, the probability to find the correct MTD should be reasonably high. Third, not too many patients should be exposed to overly toxic doses. And fourth, the approach should allow for the inclusion of relevant study-external information, such as pre-clinical data or data from other human studies.
The methodological and practical aspects of the Bayesian approach to dose finding trials in Oncology phase I will be discussed, and examples from actual trials will be used to illustrate and highlight important issues. The presentation concludes with a discussion of the main challenges for a large-scale implementation of innovative clinical trial designs in the pharmaceutical industry.