Adaptive treatment allocation for comparative clinical studies with recurrent events data

Jingya Gao, Pei Fang Su, Feifang Hu, Siu Hung Cheung

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)183-196
出版狀態Published - 2020 3月 1

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 生物化學、遺傳與分子生物學 (全部)
  • 免疫學與微生物學 (全部)
  • 農業與生物科學 (全部)
  • 應用數學


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