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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

612
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
612
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.4K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

198
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
198
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
8.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

258
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
258

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

The effect of probiotic supplements on cognitive outcomes and neuroplasticity in elderly ischemic stroke survivors.

Frontiers in neurology·2026
Same author

Combination of Yaobitong capsules and lumbar oblique pull manipulation for moderate pain in lumbar disc herniation with radiculopathy: a multicenter, randomized, three-arm, parallel-group controlled trial.

Frontiers in neurology·2026
Same author

Lipopolysaccharide promotes rheumatoid arthritis by enhancing Wnt7b mediated macrophage-FLS communication.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

OTUB1 non-canonically inhibits TAB2 ubiquitination to govern microglia-mediated neuroinflammation.

EMBO molecular medicine·2026
Same author

Endovascular treatment versus standard medical management in primary M3 or M4 occlusion stroke in clinical practice: the oriental-MeVO registry.

BMC neurology·2026
Same author

Impact of COVID-19 on Movement Disorders Patients in the Outpatient Setting.

Cureus·2026

相关实验视频

Updated: Sep 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

贝叶斯预测式联合模型,用于纵向和半竞争风险的变化系数数据数据.

Feng Gu1, Jiaqing Chen1,2, Jinjing Wang1

  • 1College of Mathematics and Statistics, Wuhan University of Technology, Wuhan, China.

Statistics in medicine
|August 7, 2025
PubMed
概括

这项研究引入了一种新的联合模型,用于分析临床研究中的纵向和半竞争性风险数据. 提出的基于预测回归的方法提高了参数估计的准确性,并减少了复杂生存数据的计算负载.

关键词:
贝叶斯的推理 贝叶斯的推理预期回归的回归是预期的回归.纵向数据 纵向数据 纵向数据半竞争性风险 半竞争性风险时间变化的系数.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Sep 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 生物统计学 生物统计学
  • 临床医学研究 临床医学研究
  • 生存分析的分析.

背景情况:

  • 半竞争性风险数据在临床研究中很常见,但在联合建模中没有得到充分研究.
  • 现有的方法往往缺乏灵活性,以适应纵向和生存数据中的时间变化的关系.

研究的目的:

  • 为纵向和半竞争性风险数据提出灵活的联合模型.
  • 用非参数函数来进行增强的建模,将时间变化的系数纳入.
  • 开发一个强大的贝叶斯推理方法,用于参数估计.

主要方法:

  • 使用预测回归的线性混合效应纵向子模型的制定.
  • 在半竞争性风险框架内开发一个Cox比例危险生存子模型.
  • 时间变化的系数和非参数函数的集成,以链接子模型.
  • 应用同步贝叶斯推理用于参数估计.

主要成果:

  • 拟议的联合模型有效地处理纵向和半竞争性风险数据.
  • 贝叶斯推理方法克服了趋同问题,提高了估计准确度.
  • 模拟研究证实了该模型的强大性能.
  • 现实世界的数据分析证明了它的实际应用性.

结论:

  • 新的联合模型为分析复杂的临床数据提供了灵活而准确的方法.
  • 同时贝叶斯推理方法提供了计算优势和可靠的参数估计.
  • 这种方法在纵向研究中推进了半竞争性风险的分析.