The value of information and optimal clinical trial design

Abstract

Traditional sample size calculations for randomized clinical trials depend on somewhat arbitrarily chosen factors, such as type I and II errors. Type I error, the probability of rejecting the null hypothesis of no difference when it is true, is most often set to 0.05, regardless of the cost of such an error. In addition, the traditional use of 0.2 for the type II error means that the money and effort spent on the trial will be wasted 20 per cent of the time, even when the true treatment difference is equal to the smallest clinically important one and, again, will not reflect the cost of making such an error. An effectiveness trial (otherwise known as a pragmatic trial or management trial) is essentially an effort to inform decision-making, i.e. should treatment be adopted over standard? As such, a decision theoretic approach will lead to an optimal sample size determination. Using incremental net benefit and the theory of the expected value of information, and taking a societal perspective, it is shown how to determine the sample size that maximizes the difference between the cost of doing the trial and the value of the information gained from the results. The methods are illustrated using examples from oncology and obstetrics.

Using Value-of-Information Methods when the Disease Is Rare and the Treatment Is Expensive—The Example of Hemophilia A

“…Recent articles about VOI methodology have tested the impact of other parameters and assumptions not considered in this study. [5][6][7]15 Our results can be used to help plan RCTs in other rare diseases with high treatment costs. As an example, the perpatient annual cost of enzyme replacement in Gaucher disease with imiglucerase is 70,100, with agalsidase in Fabry disease it is 109,600, and with laronidase in mucopolysaccharidosis 1, it is approximately 311,000.…”

“…Grounded in decision theory, VOI methods can be used to assess the uncertainty associated with the existing evidence in the comparison of two competing interventions and identify optimal sample sizes if future research is still needed. [5][6][7][8][9][10][11][12][13][14][15][16] The methods allow determination of the expected value of conducting additional research (e.g., a clinical trial) in monetary terms. The value of additional research, referred to as the expected value of sample information (EVSI), is the amount by which it reduces the expected opportunity loss associated with making decisions in the face of uncertainty.…”

A Practical Guide to Value of Information Analysis

“…Therefore, the beneficial population is usually reduced by the numbers of patients enrolled in a study [18,26]. Likewise, patients enrolled in the 'inferior' arm of a study incur an opportunity cost equal to the foregone INB per patient (which is usually added to the total cost of conducting the study).…”

“…Willan and Pinto [26] provide a comprehensive approach to calculating the EVSI. A simpler notation can be derived from Eq.…”

Bayesian statistics in medicine: a 25 year review

“…Bayesian methods provided a way of analysing rate-ratios of repeated events in cluster-randomized trials against trachoma [456]. Ideas of value of information were used to choose sample size [457]. A cost-related approach was used to evaluate whether to continue with a drug development program [458], and costs of technologies such as asthma treatments were modelled using ideas from analyses of extreme values [459].…”