Feasibility Study: A Mathematical Language for Complex Healthcare Interventions

Randomised clinical trials are the gold standard in assessing new, or competing, treatments in healthcare. It is well known that they are used in comparing new pharmaceutical treatments, where the most common type of trial compares a new drug with a standard. However, randomised trials are increasingly widely used in other areas such as psychiatry, surgery and social care. In these areas the healthcare interventions to be assessed are often more complex, being made up of different combinations of components. For example, the effectiveness of a psychiatric intervention might depend on the form in which written material is presented, the length of time of the course of treatment and the therapist. Planning such trials can be more complex than simply randomly allocating subjects to treatments, especially where there are some restrictions in the randomisation, e.g. all patients at one centre might have to see the same therapist, but can be given written material in different forms. Therefore methods for planning such trials and for analysing the data collected from them have to be chosen carefully in order to make sure all the required information is discovered and obtained without bias.

Such complex interventions can be classified and described using the mathematical language of the statistical design of experiments. This is widely used in experiments in engineering, laboratory science and elsewhere, but rarely in clinical trials. The aim of this project is to map the different types of clinical trials with complex interventions onto this mathematical framework. This will allow us to identify which methods are needed to design and analyse particular types of trial. These methods will then be translated back into the language understood by clinical triallists, so that they can be adapted to use in future trials.

It is expected that this work will lead to us identifying some gaps in the methodology of the design of experiment for some types of clinical trial. This will therefore be used to guide future research in the methodology of the design of experiments, as well as in the application to clinical trials.

In the long term, this work will allow healthcare researchers to speed up the testing of new interventions and obtain more robust evidence that they will work in a range of real-life scenarios.