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

408
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...
408
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

121
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.
121
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

214
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
214
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

176
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...
176
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

191
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...
191
Cancer Survival Analysis01:21

Cancer Survival Analysis

342
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
342

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

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same author

Dengue Vaccine Effectiveness: Results from a 6-Year Population-Based Cohort Study in Southern Brazil.

The American journal of tropical medicine and hygiene·2026
Same author

Dengue Incidence Following Mass Vaccination: An Interrupted Time Series Study in Paraná, Brazil.

Tropical medicine and infectious disease·2026
Same author

Coral Bleaching: The Equatorial-Refugia Hypothesis.

Global change biology·2025
Same author

Bayesian inference for nonlinear mixed-effects location scale and interval-censoring cure-survival models: An application to pregnancy miscarriage.

Statistical methods in medical research·2025
Same author

A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.

Statistics in medicine·2025
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

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

3.3K

用INLA进行贝叶斯生存分析.

Danilo Alvares1, Janet van Niekerk2, Elias Teixeira Krainski2

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistics in medicine
|June 26, 2024
PubMed
概括
此摘要是机器生成的。

本教程展示了使用R包集成嵌套拉普拉斯近似法 (INLA) 拟合贝叶斯生存模型. 它涵盖了各种模型,为生存分析中的快速和准确的贝叶斯推理提供了语法示例.

关键词:
贝叶斯的推理 贝叶斯的推理在这里,INLALA.在R-Packages中,使用的是R-Packages.联合建模 联合建模时间到事件分析分析.

更多相关视频

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.4K
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.4K

相关实验视频

Last Updated: Jun 23, 2025

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

3.3K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.4K
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.4K

科学领域:

  • 生物统计学 生物统计学
  • 计算统计学 计算统计学
  • 生存分析的分析.

背景情况:

  • 贝叶斯生存分析为建模时间到事件数据提供了一个灵活的框架.
  • 传统的方法可能是计算密集型,特别是复杂的模型.
  • 集成嵌套拉普拉斯近似 (INLA) 为贝叶斯推理提供了一个有效的替代方案.

研究的目的:

  • 提供清晰的语法示例,以适应使用INLA的各种贝叶斯生存模型.
  • 为了说明INLA和INLA联合R包用于生存数据分析的应用.
  • 为纵向半连续标记,反复事件和终端事件展示一种新的联合模型.

主要方法:

  • 使用集成嵌套拉普拉斯近似 (INLA) 进行近似贝叶斯推理.
  • 通过INLA和INLA联合R包实施生存模型.
  • 应用既有模型 (加速失效时间,比例危险,混合治愈,竞争风险,多状态,脆弱性) 和一个新的联合模型.

主要成果:

  • 使用INLA演示了各种贝叶斯生存模型的实际实施.
  • 为加速失效时间,比例危险,混合治愈,竞争性风险,多状态和脆弱性模型提供可重现的语法示例.
  • 成功说明了复杂的纵向和生存数据场景的新联合模型.

结论:

  • INLA为贝叶斯生存分析提供了一个快速而准确的方法.
  • INLA和INLA联合R套餐促进了先进的生存模型的实施.
  • 本教程是研究人员将贝叶斯方法应用于生存数据的实用指南.