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A multi-treatment response adaptive design for ordinal categorical responses

机译:序列分类响应的多处理响应自适应设计

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A multi-treatment response adaptive procedure is developed considering "comparison with the best" philosophy of multiple comparison procedures for clinical trials with ordinal categorical responses, when there is no shared control. For each response category, we arbitrarily create two outcome groups; one by combining the categories more favorable to it and the other by merging the categories at most as favorable to it and hence define the odds (i.e. cumulative odds). Pairwise ratios of such odds (i.e. cumulative odds ratios) based on pairs of treatments are combined conveniently to derive a measure of relative superiority and hence the allocation probability functions. For practical implementation, we suggest response-adaptive randomization (RAR), that is, update the allocation probabilities dynamically using the available allocation and response information to favor the treatment doing better. Empirical as well as large sample properties following the randomization are investigated in detail. Moreover, a real clinical trial with trauma patients is redesigned using the proposed RAR to envisage practical applicability.
机译:考虑到多种比较程序的临床试验的多种比较程序的哲学,开发了一种多处理响应自适应程序,当没有共用控制时,临床试验的临床试验。对于每个响应类别,我们任意创建两个结果组;一个通过将这些类别组合到它,另一个通过将类别合并到其最有利,因此定义了赔率(即累积赔率)。基于对处理的这种赔率(即累积大量比率)的成对比是方便地组合的,以导出相对优势的衡量标准,从而获得分配概率功能。为了实际实施,我们建议响应 - 自适应随机化(RAR),即,使用可用的分配和响应信息动态更新分配概率,以帮助治疗更好。对随机化后的经验和大的样品性质进行详细研究。此外,使用所提出的RAR来预测具有创伤患者的真正临床试验,以设想实际适用性。

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