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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Censoring Survival Data01:09

Censoring Survival Data

131
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...
131
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

474
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...
474
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

235
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
235
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

155
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.
155

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

Prohibitins in cancer: Multifaceted regulators of mitochondrial homeostasis, oncogenic signaling, tumor microenvironment, and therapeutic resistance.

Cancer genetics·2026
Same author

A Nested Copula Model for Recurrent Gap Times With a Dependent Terminal Event.

Statistics in medicine·2026
Same author

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same author

Interpretable Deep Regression Models With Interval-Censored Failure Time Data.

Statistics in medicine·2026
Same author

Mixed membership latent variable model with unknown factors, factor loadings and number of extreme profiles.

Biometrics·2026
Same author

Oxidative stress imbalance and cellular damage mediated by the ND4 G11778A mutation.

Scientific reports·2026

相关实验视频

Updated: Jul 19, 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

半参数探头回归模型与错误分类的当前状态数据.

Lijun Fang1, Shuwei Li1, Liuquan Sun2

  • 1School of Economics and Statistics, Guangzhou University, Guangzhou, China.

Statistics in medicine
|August 13, 2023
PubMed
概括

本研究引入了一种新的半参数探头模型,用于分析错误分类的当前状态数据. 提出的预期最大化算法提供了准确的回归参数估计,优于忽略数据错误的方法.

关键词:
在EM算法中,EM算法当前状态数据当前状态数据时间间隔审查的数据.最大的概率估计估计.这是错误的分类错误.

更多相关视频

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
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: Jul 19, 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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
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
  • 生存分析的分析.
  • 流行病学 流行病学

背景情况:

  • 在医学研究中常见的当前状态数据,通常包含由于不完善的诊断测试而错误分类的失败时间.
  • 现有的统计模型可能无法充分解决错误分类的当前状态数据的复杂性.

研究的目的:

  • 开发和评估一种新的半参数探头回归模型,用于分析错误分类失效时间的当前状态数据.
  • 为故障时间数据分析提供一个强大的统计框架,当确切的故障时间未知并且分类可能是错误的.

主要方法:

  • 使用非参数最大概率估计.
  • 开发了一个期望最大化 (EM) 算法,结合了泛化的池-邻近-违规者 (PAV) 算法,以处理难以处理的概率函数.
  • 拟议方法的性能通过模拟研究进行了评估,并应用于真实世界的克拉米迪亚感染数据集.

主要成果:

  • 拟议的半参数探针模型为回归参数提供了一致的,不对称的正常和半参数有效的估计器.
  • 模拟结果表明,开发的方法在有限的样本中表现良好,并且优于忽视错误分类的天真方法.
  • 该方法已成功应用于分析克拉米迪亚感染数据,展示了其实际实用性.

结论:

  • 半参数探头模型为分析错误分类的当前状态数据在故障时间分析中提供了有价值的替代方案.
  • 预期最大化算法为实现这种统计方法提供了一个可靠的计算工具.
  • 这种方法提高了在处理不完整数据的流行病学和生物医学研究中的统计推断的准确性.