文献详情
Adaptive treatment allocation for comparative clinical studies with recurrent events data
文献类型：期刊
期刊名称：Biometrics影响因子和分区
年：2020
卷：76
期：1
页码：183-196
ISSN：1541-0420
关键词：adaptive design,allocation rule,doubly adaptive biased coin design,negative binomial model
所属部门：统计学院
摘要：In long-term clinical studies, recurrent event data are sometimes collected and used to contrast the efficacies of two different treatments. The event reoccurrence rates can be compared using the popular negative binomial model, which incorporates information related to patient heterogeneity into a data analysis. For treatment allocation, a balanced approach in which equal sample sizes are obtained for both treatments is predominately adopted. However, if one treatment is superior, then it may b ...More
In long-term clinical studies, recurrent event data are sometimes collected and used to contrast the efficacies of two different treatments. The event reoccurrence rates can be compared using the popular negative binomial model, which incorporates information related to patient heterogeneity into a data analysis. For treatment allocation, a balanced approach in which equal sample sizes are obtained for both treatments is predominately adopted. However, if one treatment is superior, then it may be desirable to allocate fewer subjects to the less-effective treatment. To accommodate this objective, a sequential response-adaptive treatment allocation procedure is derived based on the doubly adaptive biased coin design. Our proposed treatment allocation schemes have been shown to be capable of reducing the number of subjects receiving the inferior treatment while simultaneously retaining a test power level that is comparable to that of a balanced design. The redesign of a clinical study illustrates the advantages of using our procedure.? 2019 The International Biometric Society. ...Hide

DOI：10.1111/biom.13117
百度学术：Adaptive treatment allocation for comparative clinical studies with recurrent events data
语言：外文
作者其他论文



Covariate-adaptive designs with missing covariates in clinical trials.Liu ZhongQiang;Yin JianXin;Hu FeiFang.SCIENCE CHINA-MATHEMATICS.2015,58(6),1191-1202.