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 Ratio01:12

Hazard Ratio

106
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...
106
Hazard Rate01:11

Hazard Rate

96
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...
96
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

166
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...
166
Odds Ratio01:09

Odds Ratio

115
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
115
Relative Risk01:12

Relative Risk

129
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
129
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

114
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.
114

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

Sample Size Recalculation in Adaptive Group Sequential Study Designs for Comparing Restricted Mean Survival Times.

Statistics in medicine·2026
Same author

Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction.

Statistics in medicine·2026
Same author

Beyond Bonferroni: new multiple contrast tests for time-to-event data under non-proportional hazards.

Lifetime data analysis·2026
Same author

Early and Late Buzzards: Comparing Different Approaches for Quantile-Based Multiple Testing in Heavy-Tailed Wildlife Research Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation Study.

Entropy (Basel, Switzerland)·2025
Same author

Lingual artery thrombosis as a presentation of infective endocarditis in a pregnant patient: a case report.

European heart journal. Case reports·2025
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
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 <math><semantics><mi>Δ</mi> <annotation>$\Delta$</annotation></semantics></math> Mutant Data.

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

相关实验视频

Updated: Jun 17, 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

10.1K

在不成比例的危险下使用平均危险比率计算样本大小.

Ina Dormuth1, Markus Pauly1,2, Geraldine Rauch3,4

  • 1Department of Statistics, TU Dortmund University, Dortmund, Germany.

Biometrical journal. Biometrische Zeitschrift
|August 12, 2024
PubMed
概括
此摘要是机器生成的。

平均危险比率 (AHR) 为具有不成比例危险的临床试验提供了一个强大的替代传统危险比率. 基于模拟的AHR测试样本大小计算提高了统计能力,提高了样本效率.

关键词:
测量效果测量效果测量方法危险比率的危险比率是什么登录排名测试试验 登录排名测试样本的大小 样本大小模拟研究是一种模拟研究.生存分析,生存分析.时间到事件数据.

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

2.0K
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

14.4K

相关实验视频

Last Updated: Jun 17, 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

10.1K
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
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

14.4K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验设计 临床试验设计
  • 生存分析的分析.

背景情况:

  • 在临床试验中,时间到事件的终点至关重要.
  • 危险比率 (HRs) 是常用的,但假设比例危险 (PHs).
  • 不成比例的危险 (N-PHs) 限制了标准HRs的解释性和适用性.

研究的目的:

  • 引入并促进实际应用平均危险比率 (AHR) 作为影响度量.
  • 开发和评估用于AHR测试的样本大小计算方法.
  • 在风险不成比例的场景中解决HR的局限性.

主要方法:

  • 开发了用于AHR测试的样本大小计算方法.
  • 进行了广泛的模拟研究,以评估样本大小计算可靠性.
  • 模拟涵盖了各种生存和审查分布,包括比例和非比例的危险.

主要成果:

  • 平均危险比率 (AHR) 可以有效地处理时间变化的影响,而不需要相应的危险.
  • 基于模拟的样本大小计算方法对于设计使用N-PHs的临床试验是可靠的.
  • 与传统方法相比,利用AHR可以提高统计能力和更高效的样本大小.

结论:

  • 平均危险比率 (AHR) 是分析时间到事件数据的宝贵工具,特别是当危险不成比例时.
  • 基于模拟的样本大小计算提高了使用AHR的临床试验的设计.
  • 通过AHR,可以更强大,更有效地检测到时间到事件结果中的群体差异.