生物标记驱动的篮子试验设计:起源和新方法的发展
1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
Journal of biopharmaceutical statistics
|June 4, 2024
概括
本综述探讨了精准医学的篮子试验,检查了传统设计,挑战以及分子驱动的癌症治疗的贝叶斯式和频率主义的新型适应.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
背景情况:
- 基因测序的进步加深了对癌症的分子理解,影响了诊断和治疗反应评估.
- 精准医学旨在使用分子驱动干预和预测生物标志物量身定制治疗.
- 篮子试验是评估各种癌症类型的向治疗的关键设计,无论瘤的位置如何.
研究的目的:
- 为传统篮子试验设计提供全面的概述.
- 识别和讨论与现有的篮子试验方法相关的实际挑战.
- 从贝叶斯和古典频率主义的观点来审查篮子试验的最新创新性适应.
主要方法:
- 对传统和新的篮子试验设计的文献综述.
- 将新的适应归类为贝叶斯式和古典频率主义方法.
- 分析实际挑战,并为实施篮子试验提出解决方案.
主要成果:
- 介绍了传统的篮子试验设计及其固有的局限性.
- 包括贝叶斯和频率主义方法在内的新改编,为设计挑战提供了潜在的解决方案.
- 该审查综合了评估生物标志物向疗法的进展.
结论:
- 较新的篮子试验设计旨在提高精准医学研究的效率和有效性.
- 了解这些新型设计的优点和局限性对于未来的临床试验开发至关重要.
- 这些适应对促进分子向癌症治疗的目标有前途.
更多相关视频
07:35Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
7.5K
07:40Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
Published on: March 20, 2021
16.7K
相关概念视频
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
