IDENT

Improving Design and analysis of oncology trials 

Evaluating New targeted Therapies

IDENT, a CRUK-funded research project

[01 May 2021 - 30 Apr 2024]

Keywords:

Adaptive design; Borrowing strength; Commensurability; Master protocol; Precision medicine

Research output to date:

9 peer-reviewed publications;

5 submitted/pre-prints;

3 brand-new short courses;

1 web application;

13 invited talks/seminars/webinar;

6 contributed conference presentations; 

6 poster presentations.

Summary of research in plain English

Clinical trials are used to test the risk and benefit of a new therapy before it can be made widely available. These trials have been designed to test if the ‘average’ patient benefits. However, in trials of new cancer treatments, patients can respond very differently to the same treatment. This is often because tumours have different 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. My work will focus on a new trial approach called ‘basket trials’, which can enrol patients of different cancer types, for example, lung cancer and breast cancer that share similar genetic profiles, to receive the same anti-cancer therapy. There are two main advantages of basket trials. Firstly, it is easier, quicker and cheaper to run a basket trial than assess each cancer location separately. Secondly, basket trials allow patient information to be fully used for understanding how the therapy works in different cancer types. Current approaches often

1) analyse each part of a basket trial separately,

2) ask for a maximum number of patients to establish the efficacy, which is inefficient and exposes more patients to an unproven treatment than might be necessary, and

3) do not allow making changes as the trial continues.


My statistical expertise will help improve basket trials to find effective cancer treatments. I will develop analysis models to make better use of basket trial data. They will allow sharing of information between similar cancer types. This will mean that we get a better understanding about which treatments work, and for whom. These new statistical approaches will make a difference to the way we design basket trials. I will propose new methods to calculate the required number of patients to recruit in a basket trial. Because information from similar parts can be combined, a considerably smaller number of patients will be needed. More flexible designs will also be developed to allow making changes as the basket trial continues. For example, it would be possible to stop some parts earlier than planned, if evidence shows a meaningful improvement in patients’ outcome. As a result, basket trials can be completed quicker and at a lower cost, allowing new therapies to go from creation to use in practice considerably quicker. In this way, cancer patients can be assured to be treated much quicker and with more effective therapies.