Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents
精准医疗中基于生物标志物的贝叶斯Ⅰ/Ⅱ期剂量探索AI 解读 ↓
← 返回论文列表与筛选
阅读说明
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。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究临床试验的个体化剂量探索，未提供与所列计量领域细分直接对应的依据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
精准医疗中基于生物标志物的贝叶斯Ⅰ/Ⅱ期剂量探索
- 研究问题
- 面对多个生物标志物及其与剂量的交互作用，如何为接受分子靶向药物的患者确定兼顾毒性和疗效的个体化剂量？
- 方法与思路
- 作者以典型偏最小二乘提取低维成分，用潜变量模型描述有序毒性与疗效，并结合效用函数实施两阶段剂量探索。
- 材料陈述的贡献
- 摘要称，设计可依据患者的生物标志物配置寻找个体化最优剂量，模拟显示较好的运行特性和剂量识别概率。
- 阅读关注点
- 应核对降维成分的构造、毒性与疗效模型、效用权重、两阶段分配规则，以及模拟情景与安全性约束。
- 阅读边界
- 依据稿件版本未注明的 OpenAlex 索引摘要；模拟概率、模型稳健性及临床验证未提供，不能据此确认实际医疗效果。
原文摘要
Beibei Guoa* & Ying Yuanba Department of Experimental Statistics, Louisiana State University, Baton Rouge, LAb Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TXCONTACT Beibei Guo beibeiguo@lsu.edu Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTThe optimal dose for treating patients with a molecularly targeted agent may differ according to the patient's individual characteristics, such as biomarker status. In this article, we propose a Bayesian phase I/II dose-finding design to find the optimal dose that is personalized for each patient according to his/her biomarker status. To overcome the curse of dimensionality caused by the relatively large number of biomarkers and their interactions with the dose, we employ canonical partial least squares (CPLS) to extract a small number of components from the covariate matrix containing the dose, biomarkers, and dose-by-biomarker interactions. Using these components as the covariates, we model the ordinal toxicity and efficacy using the latent-variable approach. Our model accounts for important features of molecularly targeted agents. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose according to each patient's individual biomarker profile. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose. Supplementary materials for this article are available online.