Most clinical trials are designed with no reference to the size of the population in which the research is conducted. Whilst this may be reasonable in a large population, in rare diseases or other small populations it can lead to designs that are inappropriate. If the population under investigation is small, a large proportion of the patient group may be recruited to a clinical trial. Recruitment to one trial may thus have an impact on the conduct of other trials or even reduce significantly the size of the population receiving usual care. The value of one clinical trial must therefore be compared with that of other trials if research is to proceed efficiently. We will develop novel methods for the design and sequential monitoring of small population clinical trials based on a formal comparison of the gain in information from a clinical trial with the cost of the trial.
Decision-theoretic methods explicitly enable evaluation of the level of evidence required from a clinical trial to best inform clinical practice. This in turn can lead to efficient and appropriate clinical trial design. In a small population setting, the small available sample size, the fact that recruitment to one clinical trial may limit recruitment to concurrent trials and the small size of the target population may mean that standard clinical trial designs, proposed in the setting of large patient populations, may be inappropriate. In this project we will explore the use of decision-theoretic and value-of-information methods for the design and sequential monitoring of clinical trials in small populations. This will lead to smaller studies that are more able to lead to appropriate decision-making.
The methodology developed will also build on the health economic value of information approach, and enable an assessment of the appropriate level of information required for clinical decision-making in the small population setting. Value-of-information methods have been explored extensively in the health economic literature, where value is defined in terms of the likelihood of adoption decisions changing in light of trial results, and is therefore linked to decision-making concerning the reimbursement of drugs. Extending this approach to rare diseases and small populations would be particularly novel and challenging, since reimbursement decisions involve balancing cost-effectiveness against complex and subjective considerations of equity.