Value of information methods to design a clinical trial in a small population to optimise a health economic utility function

Abstract

BackgroundMost confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population.MethodsWe considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future.ResultsThe optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored.ConclusionsDecision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0475-0) contains supplementary material, which is available to authorized users.

Approaches to sample size calculation for clinical trials in rare diseases

“…In this situation with a normally distributed prior and observations, the gain function can be computed to have a quite simple form. We show this in Appendix A following the computation by Willan and Pearce et al in a similar situation but for an acute treatment. Optimizing this computed gain function over the sample size, we obtain the decision‐theoretic sample size as n * = 221.…”

“…Several criteria to ensure limited length of posterior credibility intervals can be defined to determine a Bayesian sample size; see Joseph and Bélisle for normal means and their differences and M'Lan, Joseph, and Wolfson for the binomial case. Moreover, aspects from different approaches might be combined; eg, a significance test to decide upon treatment recommendation could be incorporated into the decision‐theoretic frame . Uncertainties about cost parameter or recruitment parameter might be handled using prior distributions for these parameters as well.…”

“…In the investigated case studies, the parameter for the additional treatment cost of the new treatment and the assumed difference in prior means were parameters highly influencing the sample size for the decision‐theoretic approach. The importance of the treatment cost parameter for sample size and treatment decision making was highlighted by Pearce et al…”

“…Moreover, aspects from different approaches might be combined; eg, a significance test to decide upon treatment recommendation could be incorporated into the decision-theoretic frame. 26,31,32 Uncertainties about cost parameter or recruitment parameter might be handled using prior distributions for these parameters as well.…”

The multiple benefits of sport in haemophilia

“…The authors describe a complex multiple imputation (MI) approach to analysing their data given the relatively high proportion of patients with missing data; a mitigation of this problem may have been possible using a recently described Value of Information method to design a clinical trial in a small population and optimize a health economic utility function. 16 Another methodological issue lies in the choice of a CUA, which, unlike other authorities in England and Australia, is not used by the German HTA authority, rather than the efficiency frontier method which is used by the German authority. 17 It is possible that the small differences in cost/QALYs would be even more obvious in this methodology than with the various costeffectiveness acceptability curves offered by the authors.…”