title: Self-designing two-stage trials to minimize expected costs.
abstract: In the design of clinical trials, the sample size for the trial is traditionally calculated from estimates of parameters of interest, such as the mean treatment effect, which can often be inaccurate. However, recalculation of the sample size based on an estimate of the parameter of interest that uses accumulating data from the trial can lead to inflation of the overall Type I error rate of the trial. The self-designing method of Fisher, also known as the variance-spending method, allows the use of all accumulating data in a sequential trial (including the estimated treatment effect) in determining the sample size for the next stage of the trial without inflating the Type I error rate. We propose a self-designing group sequential procedure to minimize the expected total cost of a trial. Cost is an important parameter to consider in the statistical design of clinical trials due to limited financial resources. Using Bayesian decision theory on the accumulating data, the design specifies sequentially the optimal sample size and proportion of the test statistic's variance needed for each stage of a trial to minimize the expected cost of the trial. The optimality is with respect to a prior distribution on the parameter of interest. Results are presented for a simple two-stage trial. This method can extend to nonmonetary costs, such as ethical costs or quality-adjusted life years.
authors.affiliation: Merck Research Laboratories, Rahway, New Jersey 07065-0900, USA. chau_thach@merck.com
authors.forename: Chau T
authors.initials: CT
authors.lastname: Thach
authors.forename: Lloyd D
authors.initials: LD
authors.lastname: Fisher
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D002986:Clinical Trials as Topic / Q000191:economics* / Q000706:statistics & numerical data*; D003365:Costs and Cost Analysis; D003662:Decision Theory; D006801:Humans; D012680:Sensitivity and Specificity