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

Hazard Rate01:11

Hazard Rate

104
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...
104
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

123
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.
123
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

177
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...
177
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

351
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
351
Hazard Ratio01:12

Hazard Ratio

114
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
114
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125

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

Use of a polygenic risk score to enhance early detection of coronary atherosclerosis.

American journal of preventive cardiology·2026
Same author

Wqsreg: a Stata command for weighted quantile sum regression.

European journal of epidemiology·2026
Same author

Total Event Analysis in Cardiovascular Outcome Trials: Approaches and Interpretation.

Circulation·2026
Same author

Effects of SGLT2 inhibition on incident heart failure in carriers of cardiomyopathy-associated genetic variants.

Nature medicine·2026
Same author

Evolocumab in Patients With High-Risk Diabetes: Results From the VESALIUS-CV Trial.

Diabetes care·2026
Same author

Lipoprotein(a) Levels, Risk of Cardiovascular Events, and Benefit of Evolocumab: Findings From the VESALIUS-CV Trial.

Circulation·2026

相关实验视频

Updated: Jun 25, 2025

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

2.0K

从具有复杂非线性相互作用的考克斯模型中估计和呈现危险比率和绝对风险.

Andrea Bellavia1, Giorgio E M Melloni1, Jeong-Gun Park1

  • 1TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States.

American journal of epidemiology
|May 22, 2024
PubMed
概括

这项研究引入了一种分析生存数据相互作用的新方法,提高了健康研究的精度. 它可以更准确地评估多种因素如何影响临床结果,无论是乘法还是加法尺度.

关键词:
考克斯回归法 考克斯回归法互动互动互动互动互动.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,斯普林斯生存分析,生存分析.

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

10.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

252

相关实验视频

Last Updated: Jun 25, 2025

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

2.0K
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

10.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

252

科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 临床研究 临床研究

背景情况:

  • 相互作用分析在临床和公共卫生研究中对于精准医学至关重要.
  • 当前的方法往往过于简化了相互作用,限制了精度和信息,特别是在时间到事件分析中.
  • 现有的方法通常侧重于乘法尺度,忽视临床相关的绝对风险信息.

研究的目的:

  • 提出一个用户友好的程序来估计和呈现生存分析中的交互效应.
  • 为了能够在乘法和加法两种尺度上对相互作用进行评估.
  • 为了方便灵活地纳入与连续共变量的非线性相互作用.

主要方法:

  • 利用来自考克斯模型的个体绝对风险预测.
  • 开发一种使用连续共变量进行灵活相互作用评估的程序.
  • 提供复制软件,并讨论信心区间推导.

主要成果:

  • 拟议的方法允许更精确地估计互动效应.
  • 它可以在乘法和加法两种尺度上呈现结果.
  • 这种方法适应了连续共变量的非线性相互作用.

结论:

  • 新的程序提高了生存分析中复杂的共同变量关系的评估.
  • 它提供了更直观和精确的交互和效果分层结果的描述.
  • 这有助于更好地了解公共卫生和临床研究中的临床终点.