Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Biostatistics: Overview01:20

Biostatistics: Overview

732
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
732
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

17.5K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
17.5K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

577
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
577

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Racial and Ethnic Disparities in Length of Stay for Pediatric Bacterial Tracheostomy-Associated Infections.

Hospital pediatrics·2026
Same author

Cognitive performance modulates regional brain age differences in clinical anxiety and depression.

Journal of affective disorders·2026
Same author

Introducing the SAGE study: a multimodal protocol for testing a GABAergic mechanism of age-related episodic memory impairment across the sexes.

BMJ neurology open·2026
Same author

A Pragmatic SMART Study of Medication and CBT Sequencing in Pediatric Anxiety Disorders: A Randomized Clinical Trial.

The American journal of psychiatry·2026
Same author

Huyan-I formula attenuates renal senescence and fibrosis by inhibiting STAT3/NF-κB/NLRP3-driven SASP.

Journal of natural medicines·2026
Same author

Exceptionally low mortality despite widespread COVID-19 infection among Indigenous Tsimane and Moseten of Bolivia.

Social science & medicine (1982)·2026

相关实验视频

Updated: Jan 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

对连续生物标记物的贝叶斯适应性丰富设计.

Yue Tu1, Yusha Liu2, Wendy J Mack1

  • 1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.

Statistics in medicine
|September 14, 2025
PubMed
概括

本研究介绍了癌症治疗中连续生物标志物的适应性临床试验设计. 它通过利用完整的生物标记信息来提高效率和以患者为中心的决策,与简化标记关系的方法不同.

关键词:
贝叶斯适应式设计是贝叶斯的适应式设计.适应性丰富设计的设计.适应性随机化适应性随机化基于生物标志物的设计.临床试验临床试验临床试验临床试验临床试验精准医学是一门精准医学.

更多相关视频

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

611
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

相关实验视频

Last Updated: Jan 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

611
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 精准医学是一门精准的医学.

背景情况:

  • 在癌症中,精准医学依赖于针对性治疗的预测生物标志物.
  • 当前的临床试验设计往往将连续生物标志物二分为二,从而丢失了有价值的信息.
  • 现有的方法可能过于简化了复杂的生物标志物治疗相互作用.

研究的目的:

  • 为连续生物标志物提出一种新的适应性丰富试验设计.
  • 以适应任何效果形状 (线性,非线性,非单调) 的生物标志物.
  • 提高癌症临床试验的统计效率和以患者为中心的决策.

主要方法:

  • 开发一个贝叶斯标记-适应性随机化设计.
  • 在没有前期二分化的情况下处理连续预测生物标志物.
  • 与缺乏自适应随机化的自适应切点选择方法进行比较.

主要成果:

  • 拟议的设计有效地处理具有不同效果形状的连续生物标志物.
  • 它在做出特定标志物试验决策时实现了高效率.
  • 与简化切割点选择方法相比,表现出更好的性能.

结论:

  • 适应性丰富设计与贝叶斯标记-适应性随机化对连续生物标记者是有效的.
  • 这种方法保留了来自连续生物标志物的信息,从而导致更好的试验结果.
  • 为临床试验中的传统生物标志物处理提供了更加复杂和以患者为中心的替代方案.