Combining Optimization and Randomization Approaches for the Design of Clinical Trials

t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients’ attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality – the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling – the absence of a tendency to allocate each pair of patients consistently to the same arm

Problems with Constructing Tests to Accept the Null Hypothesis

Futility designs bhaveeen proposed and used by constructing classical (non-Bayesian) hypothesis tests such that the decision of therapeutic interest is to accept the null hypothesis.A consequence is that the probability of accepting (failing to reject) the null when the null is false is unknown. Reversal of the conventional null and alternative hypotheses is not required either to demonstrate futility/nonsuperiority, or to align type I and II errors with their consequences. Conventional methods to test whether the response to the investigational agent is superior to a comparative control (superiority trial) are preferable and in the case that the null hypothesis is rejected, the associated type I error is known