STEEP

Statistically Efficient Methods for Precision Medicine Trials

Plain English Summary of Research

All anti-cancer treatments must go through clinical trials for thorough testing before they can be made available widely for treating patients. Using biomarkers (e.g., genetic aberrations), patients can now be stratified into small subgroups that may receive different benefits from the same treatment. Innovative approaches including basket trials and umbrella trials can evaluate a new treatment in several patient subgroups at once. Such patient subgroups typically feature certain genetic make-ups of the tumour, with each being targeted by a treatment.

Having multiple patient subgroups to evaluate the efficacy of one treatment may see positive effects claimed more often when they are in fact not clinically meaningful. In addition, advanced statistical methods that enable adaptation (e.g., allocating patients to a better-suited treatment) are needed for both ethical considerations and accelerating the process of drug development.

This 6-year research programme aims to develop, as well as to promote the implementation of, advanced statistical methods for precision oncology trials that support decisions to be made in an adaptive manner. Specifically, statistically efficient designs for modern biomarker-driven clinical trials will be developed to

1) control the risk of incorrectly claiming efficacy when there is none,

2) allocate more cancer patients to receive a better-suited treatment,

3) supplement the basket or umbrella trial data (especially for rare cancers) with historical controls or real-world evidence,

4) enable adding new patient subgroups or new control treatment(s) to an ongoing biomarker-driven clinical trial.