Novel clinical trial designs for establishing potential efficacy of complex interventions

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Obtaining evidence about whether or not a new treatment is better than currently available treatments is difficult, time-consuming and expensive. Ideally, it requires a large experiment including hundreds, or even thousands, of patients. To make sure only treatments which have a good chance of being found to have a beneficial effect undergo these expensive experiments, smaller 'exploratory' experiments are often carried out first, giving an initial indication of whether the treatment is worth studying further.When the treatment is a drug, these exploratory experiments are common, but not all treatments are drugs. One example would be a psychotherapy treatment delivered by a psychologist for patients suffering depression. Another example might be a support group like alcoholics anonymous. Other non-drug treatments (known as 'complex interventions') can include surgery, physiotherapy, occupational therapy, speech therapy or nursing. It is generally more difficult to test these complex interventions than drugs. This is partly because we are often interested in a number of ways in which the treatment affects patients, as opposed to focussing on a single measure. Another complication comes from it being difficult to work out what is having the effect - the treatment itself, or the person delivering the treatment. When designing exploratory experiments to examine complex interventions, a fundamental decision is how many patients and care providers to study. This is important because it will determine how confident researchers can be in the results of the experiment. The larger the experiment, the less likely that we will observe an extreme effect of the treatment by chance alone. However, we also want to keep experiments as small as possible to minimise the cost and participant's time. Statisticians therefore need to work out the smallest number of participants which will give the research community enough confidence in the results to allow research funders to make reliable decisions about which treatments to take on to large, expensive experiments. However, methods for doing this for exploratory experiments of complex interventions have yet to be developed.In this project I will develop these methods, enabling researchers to properly design exploratory experiments testing complex interventions in a reliable and efficient way. I will develop several variants of the methods so that they can be applied in a range of situations. I will start by extending existing methods used within drug development, focusing on keeping the chance of making an incorrect decision (e.g. taking an ineffective treatment on to a large expensive experiment) low. After this, I will consider ways to use existing information about the treatment, such as how much we expect its effect to vary among the population, which will help minimise the number of participants needed. Finally, I will develop methods that explicitly consider the consequences of decisions, leading to experiments which better reflect the priorities of all interested parties.All of these approaches will be computer intensive. To help others use them, I will also spend time writing user-friendly and efficient computer software which allows them to be used quickly and easily and will apply the methods to real examples of trial design problems. This will involve working with clinicians and other statisticians, and will help to make sure that the methods developed are easy to apply and will be used in practice.Developing these methods will improve the process of developing and testing complex interventions. They will ensure that the limited resources available for running large clinical trials (including research funds and participant time) are spent wisely on the treatments which show the most promise. In turn, this will increase the rate at which new interventions are identified and made available, improving the standard of care for patients across the breadth of the NHS.