Abstract
In the clinical trials of new drug development, patients will be divided into control group and treatment group. Let patients in control groups take old drugs or placebos and those in treatment group take new drugs. Scientists will determine whether the efficacy of new drugs are truly better through analysis. Besides selecting the drugs with best efficacy, scientists also care about controlling the experimental cost. Therefore, Cheng-Shiun Leu and Bruce Levin have designed a procedure for selecting drugs in phase-II clinical trials, which is called adaptive design in phase-II clinical trials. These researches mainly focus on the drugs whose responses are only success or failure. However, to patients with hypertension and hypercholesterolemia, their responses of taking medicine are continuous variables. In this thesis, we provide sequential procedures (non-adaptive NDD and adaptive NDD) for drugs with Normal distributed responses and define a cost function as criteria based on Bayesian. Through R language and Monte Carlo method, we study how to set the significance level and the largest number of patients to select the drugs with best efficacy using minimum costs. Finally, we verify that the adaptive NDD is better than non-adaptive NDD and solve the optimizations.