Assurance methods for adaptive clinical trial designs

When designing a clinical trial, the number of patients recruited to the trial must be planned for carefully. If there are too few, there is a risk that the trial will not provide sufficient evidence that the treatment works. If there are too many, some patients will be needlessly enrolled into a study, potentially receiving a treatment that could, at that time, be demonstrated to be ineffective.

Traditionally, to choose the number of patients, the statistical concept of 'power' is used. Informally, the power of a clinical trial is the probability that the trial will be successful and demonstrate that the treatment works. Critically, power assumes the new treatment really does work as well as desired. But we cannot know this to be true before the study starts. In practice, trials are successful significantly less frequently than predicted from power calculations, and this is very costly.
This project will develop an alternative statistical method: "assurance" that involves properly assessing uncertainty about the effectiveness of the treatment, before the trial is conducted, so that a more realistic assessment can be made of the chances of success for the trial. Specifically, assurance methods will developed for more complex types of trial design.

In the assurance method, the format of the trial and the method for analysing the trial data are first specified, as they would be for a conventional power calculation. We then 'elicit' a probability distribution from experts - supported by available evidence and expertise - to represent uncertainty about the effectiveness of the new treatment. Given this distribution, we can compute the probability the trial will be successful, allowing for our current uncertainty about how well the treatment works.
The general technique of eliciting a probability distribution from experts is used in Bayesian statistics (to obtain a 'prior' distribution) and in probabilistic risk analysis. The challenge in expert elicitation is how to convert domain-specific knowledge and uncertainty to a probability distribution, in particular when we need a distribution for some parameter in a statistical model that the experts may find difficult to assess directly. Other problems include what to do when there are different experts who disagree with each other, and how to justify the final choice of probability distribution.

There have only been a small number of publications on assurance methods, and these all assume very simple trial designs and analysis, for example, that there will be a single treatment group, a single control group, and that the data will be analyses with a solitary two-sample t-test. The aim of this project is to develop assurance methods for more complex trial designs such as adaptive designs.

The student will first conduct a literature review of clinical trial designs and existing assurance methods, and in consultation with the industrial partner, choose some clinical trial designs for the development of assurance methods. The type of data to be collected within a particular trial design will be specified, and the statistical model used to analyse the data will be determined. The uncertain parameters in the model will then be identified. For the chosen design, the deliverables will be

1. an elicitation protocol, setting out what questions to ask the (treatment-specific) experts, such that probability distributions can be constructed for all the uncertain parameters in the model.
2. specification of computational methods for calculating assurances.
3. adaptation of the current design protocol, to exploit the elicited assurance.
4. web-based apps to implement the methods, produced with the R package shiny.