title: Design of adaptive two-stage double-arm clinical trials for dichotomous variables.
abstract: It is well known that flexibility is one of the major advantages of an adaptive two-stage design, and the intended adaptation should be as preplanned as possible to maintain the integrity of the clinical trial. The design of adaptive two-stage double-arm clinical trials for dichotomous variables was proposed by simulation and forecasting procedure at the planning stage. To further ensure the integrity of the clinical trial, the sample size scheme for each scenario, which was supposed to be based on the first stage, was provided in the protocol by Monte Carlo simulation. In addition, the study parameters were determined by comparing the assessment indexes such as total sample size, expected sample size and the test power at the first stage. Furthermore, Fisher's combination test and pooled data analysis were considered and compared through the simulation. The latter, which has the larger overall power and the better overall type I error control, with the same sample size was adopted for further simulation and statistical analysis in the clinical trial.
authors.affiliation: Department of Health Statistics, Faculty of Preventative Medicine, Fourth Military Medical University, Xi'an, Shaanxi, China.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Jiang
authors.forename: Fubo
authors.initials: F
authors.lastname: Xue
authors.forename: Chanjuan
authors.initials: C
authors.lastname: Li
authors.forename: Ling
authors.initials: L
authors.lastname: Wang
authors.forename: Hongwei
authors.initials: H
authors.lastname: Cai
authors.forename: Chunmao
authors.initials: C
authors.lastname: Zhang
authors.forename: Jielai
authors.initials: J
authors.lastname: Xia
mesh_terms: D017326:Clinical Trials, Phase III as Topic; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D006801:Humans; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018401:Sample Size; D013223:Statistics as Topic