Optimal and/or Efficient Two treatment Crossover Designs for Five Carryover Models.

IF 1.2 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jigneshkumar Gondaliya, Jyoti Divecha
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引用次数: 4

Abstract

Crossover designs robust to changes in carryover models are useful in clinical trials where the nature of carryover effects is not known in advance. The designs have been characterized for being optimal and efficient under no carryover-, traditional-, and, self and mixed carryover- models, however, ignoring the number of subjects, which has significant impact on both optimality and administrative convenience. In this article, adding two more practical models, the traditional, and, self and mixed carryover models having carryover effect only for the new or test treatment, a 5M algorithm is presented. The 5M algorithm based computer code searches all possible two treatment crossover designs under the five carryover models and list those which are optimal and /or efficient to all the five carryover models. The resultant exhaustive list consists of optimal and/or efficient crossover designs in two, three, and four periods, having 4 to 20 subjects of which 24 designs are new optimal for one of the established carryover models, and 34 designs are optimal for newly added models.

五种结转模型的最佳和/或有效的两种处理交叉设计。
对结转模型变化稳健的交叉设计在结转效应的性质事先不知道的临床试验中是有用的。这些设计在无结转、传统结转、自结转和混合结转模型下具有最优和高效的特点,然而,忽略了受试者的数量,这对最优性和管理便利性都有重大影响。在本文中,增加了传统的、自的和混合的两种更实用的模型,仅对新处理或测试处理具有结转效果,提出了一种5M算法。基于5M算法的计算机代码在五种结转模型下搜索所有可能的两种处理交叉设计,并列出对所有五种结转模型最优和/或有效的设计。所得的详尽列表包括在2、3和4个时期的最优和/或有效交叉设计,有4到20个主题,其中24个设计是一个已建立的结转模型的新最优设计,34个设计是新增加的模型的最优设计。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY
CiteScore
2.10
自引率
8.30%
发文量
28
审稿时长
>12 weeks
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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