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

Censoring Survival Data01:09

Censoring Survival Data

88
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
88
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

425
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...
425
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

126
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.
126
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

183
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
183
Hazard Rate01:11

Hazard Rate

105
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
105
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

136
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,...
136

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

Corrigendum to "Maimendong decoction modulates the PINK1/Parkin signaling pathway alleviates type 2 alveolar epithelial cells senescence and enhances mitochondrial autophagy to offer potential therapeutic effects for idiopathic pulmonary fibrosis" [J. Ethnopharmacol., 345, (9 April 2025), 119568].

Journal of ethnopharmacology·2026
Same author

Manganese‑containing mesoporous bioactive glass with antioxidative and osteogenic activities for periodontitis treatment.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Mechanism of claudin-2 in RTECs apoptosis after renal obstruction.

Urolithiasis·2026
Same author

HA thermostability mutations S84F, G167N, and D168N potentiate H9N2 virus transmission in a warming environment.

Emerging microbes & infections·2026
Same author

Retraction Note: Carbon nano-onion-mediated dual targeting of P-selectin and P-glycoprotein to overcome cancer drug resistance.

Nature communications·2026
Same author

Piezo1-specific deletion in macrophage attenuates radiation-induced lung injury progression in mice.

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

相关实验视频

Updated: Jun 30, 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

10.2K

面板计数数据的条件建模与部分间隔审查的故障事件.

Xiangbin Hu1, Wen Su2, Zhisheng Ye3

  • 1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.

Biometrics
|March 18, 2024
PubMed
概括

这项研究引入了一种新的统计模型,用于分析纵向研究中的反复事件,并考虑信息失败事件. 该方法提高了对影响事件复发和故障时间的因素的理解.

关键词:
条件建模 有条件建模实证过程是经验过程.有关信息的故障时间面板计数数据数据 面板计数数据部分间隔审查的数据.

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

2.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

相关实验视频

Last Updated: Jun 30, 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

10.2K
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

2.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

科学领域:

  • 生物统计学 生物统计学
  • 纵向数据分析 纵向数据分析
  • 生存分析的分析.

背景情况:

  • 纵向研究中的面板计数数据通常涉及反复发生的事件.
  • 部分间隔审查的故障事件可以提供关于反复发生事件的信息数据.
  • 使用隐性变量模型的现有方法提供了对故障事件效应的间接解释.

研究的目的:

  • 为面板计数数据提出一个新的统计模型,提供信息,部分间隔审查的故障事件.
  • 开发一个估计程序,提供直接解释故障事件影响的估计程序.
  • 解决现有统计方法对反复事件数据分析的局限性.

主要方法:

  • 开发了一个失效时间依赖的比例平均值模型,具有未指定的链接函数.
  • 使用二阶段估计程序,使用最小平方值的有条件预期.
  • 使用B-spline函数来近似未知的基线平均值和链接函数,将故障时间分布视为麻烦参数.

主要成果:

  • 拟议的方法允许直接解释故障事件对反复事件的影响.
  • 理论导出确定了估计器的收率和非对称正常性.
  • 广泛的模拟研究证实了有限样本的性能与理论结果一致.

结论:

  • 开发的统计模型和估计程序有效地处理面板计数数据,提供信息,部分间隔审查的故障事件.
  • 该方法提供了一种更直接,更易于解释的方式来分析故障事件对循环过程的影响.
  • 这种方法在一项纵向健康长寿研究中得到了成功说明,并得出了有洞察力的结论.