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

1.0K
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...
1.0K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

401
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.
401
Hazard Rate01:11

Hazard Rate

404
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...
404
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

756
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...
756
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

565
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...
565
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

581
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,...
581

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

Blood DNA methylation and breast cancer risk: a prospective nested case-control study.

EBioMedicine·2026
Same author

Can the Contract Between Addiction Scientists and Policymakers Be Restored to What It Was in Griffith Edwards' Time?

Journal of studies on alcohol and drugs·2026
Same author

Trends of Fentanyl and Oxycodone Use in Australia (2018-2022) in National Prescription and Wastewater Data Sets.

Environmental science & technology·2026
Same author

A framework for jointly modeling the natural history of ductal carcinoma in situ and invasive breast cancer.

Journal of mathematical biology·2026
Same author

Substance use and treatment utilization patterns of working-age American men who were not in employment, education, or training (NEET) during the COVID-19 pandemic.

Substance abuse treatment, prevention, and policy·2026
Same author

TikTok is a valuable data source for tracking the opioid crisis.

NPJ digital medicine·2026
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

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

11.0K

灵活的参数加速失效时间模型与治愈.

Birzhan Akynkozhayev1, Benjamin Christoffersen1, Xingrong Liu2

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Biometrical journal. Biometrische Zeitschrift
|September 10, 2025
PubMed
概括
此摘要是机器生成的。

加速失效时间 (AFT) 模型为考克斯模型提供了一个可折叠和可解释的替代方案. 增强的AFT模型通过结合时间变化的效应和治愈模型来改善临床研究,在共变量估计中显示出稳健性.

关键词:
加速失效时间模型.治愈模型 治愈模型灵活的参数模型灵活的参数模型.斯普林斯,斯普林斯,斯普林斯,斯普林斯,斯普林斯,斯普林斯,斯普林斯,斯普林斯

更多相关视频

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

10.8K
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

2.5K

相关实验视频

Last Updated: Jan 18, 2026

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

11.0K
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

10.8K
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

2.5K

科学领域:

  • 生物统计学 生物统计学
  • 生存分析的分析.
  • 临床研究方法论 临床研究方法论

背景情况:

  • 加速失效时间 (AFT) 模型是考克斯比例危险模型的替代方案.
  • AFT模型提供可折叠的效果测量和对生存时间尺度的直接解释.
  • 最近的光滑参数 AFT 模型有局限性,需要扩展以更广泛地应用.

研究的目的:

  • 增强灵活的参数加速失效时间 (AFT) 模型,以改善临床研究应用.
  • 通过引入新功能来解决现有的光滑参数 AFT 模型的局限性.
  • 提供一个强大的和可解释的生存分析工具.

主要方法:

  • 采用单调的自然线索用于日志累积危险.
  • 集成的变时加速度因子和固化模型 (混合和非混合).
  • 在公开可用的rstpm2 R包中实现了扩展.

主要成果:

  • 模拟显示在估计治愈分数方面具有可变的成功.
  • 灵活的AFT模型表现出比Cox模型更强大的协变效应估计的稳定性,即使治疗比例很高.
  • 扩展成功地应用于现实世界的生存数据.

结论:

  • 开发的灵活的参数 AFT 模型为临床研究中的生存数据分析提供了显著的改进.
  • 这些增强的AFT模型为传统的Cox模型提供了更强大和更易于解释的替代方案,特别是在治愈的个体面前.
  • rstpm2软件包有助于应用这些先进的 AFT 模型.