Semiparametric Dose Finding Methods
半参数剂量探索方法AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要研究临床剂量设计,未陈述处理效应识别或其他所列经济计量主题。
半参数剂量探索方法AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Summary We describe a new class of dose finding methods to be used in early phase clinical trials. Under some added parametric conditions the class reduces to the family of continual reassessment method (CRM) designs. Under some relaxation of the underlying structure the method is equivalent to the cumulative cohort design, the modified toxicity probability interval method or Bayesian optimal interval design classes of methods, which are non-parametric in nature whereas the CRM class can be viewed as being strongly parametric. The class proposed is characterized as being semiparametric since it corresponds to the CRM with a nuisance parameter. Performance is good, matching that of the CRM class and improving on it in some cases. The structure allows theoretical questions to be more easily investigated and to understand better how different classes of methods relate to one another.