IDENT: Improving Design and analysis of oncology trials Evaluating New targeted Therapies

Background: Phase II clinical trials are often designed to assess whether a new therapy works in patients on average. In cancer trials, however, patients can respond very differently to the same treatment. This could be because of differences in tumour mutations.

Modern ‘targeted treatments’ are developed to target particular genetic make-up of the tumour instead of the location of the cancer in the body.

A new trial approach called ‘basket trials’ can enrol patients of various cancer types, for example, lung cancer and breast cancer patients that share similar genetic profiles.

Current approaches often 1) analyse the subtrials, e.g., different cancer types, separately, 2) require an unnecessarily large number of patients to establish efficacy, and 3) do not permit making changes as the basket trial continues. Aims: This fellowship aims to investigate how basket trials can be best designed and analysed.

Specifically, the research will propose statistical methodology that can: 1) fully utilise the subtrial data, allowing borrowing of information, to improve decision making, 2) plan basket trials with a smaller sample size, which may be re-estimated as the trial continues, 3) prioritise development paths in certain patient subgroups using adaptive designs.

Methods: I will use real cancer studies such as the MAJIC trial to motivate the methodological research for basket trials with added efficiency.

Bayesian approaches will be developed to permit information from relevant subtrials to be represented into a prior for the treatment effect. The magnitude of borrowing will depend on how consistent the treatment’s effect is.

Formulae to calculate sample size for basket trials will be derived, incorporating a new parameter for subtrial data consistency.

The level of data consistency will first be assumed as a known quantity, informed by e.g., biological knowledge, to obtain a fixed sample size when standard non-adaptive tests are used.

Adaptive designs will also be developed for basket trials to permit re-estimating the sample size as data on the consistency is gathered, in order to ensure the study power is correct. How the results of this research will be used: I will publish papers on statistical methods and their applications.

Open-source software with user-friendly web interfaces will be released.

The methods will allow a substantial gain in power and estimation accuracy for basket trials and better value for funders of trials such as CRUK. In the long term, methods will allow more efficient clinical evaluation and better treatments for cancer patients.