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

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
Hazard Rate01:11

Hazard Rate

102
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...
102
Censoring Survival Data01:09

Censoring Survival Data

76
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...
76
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

175
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...
175
Relative Risk01:12

Relative Risk

143
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...
143
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

212
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...
212

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

Analysis of the time to breast cancer diagnosis in Cuba: an observational study.

BMC public health·2025
Same author

Current status data with two competing risks and missing failure types: a parametric approach.

Journal of applied statistics·2022
Same author

Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling.

Cancer research·2020
Same author

A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right-censored survival data.

Statistics in medicine·2018
Same author

Sample size of the reference sample in a case-augmented study.

Pharmacoepidemiology and drug safety·2017
Same author

Effect of reporting bias in the analysis of spontaneous reporting data.

Pharmaceutical statistics·2014
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 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

目前状态数据有两个竞争的风险和时间依赖的缺失故障类型.

Tamalika Koley1, Anup Dewanji2

  • 1Centre for Quantitative Economics and Data Science, Birla Institute of Technology, Mesra, Ranchi, India.

Journal of applied statistics
|July 3, 2024
PubMed
概括
此摘要是机器生成的。

本研究解决了竞争性风险数据中缺失的故障类型,开发了当前状态数据的新估计方法. 这项研究提供了强大的统计技术,用于分析不确定的数据的复杂健康结果.

关键词:
监控时间 监控时间可以识别的可识别性时间间隔危险 时间间隔危险掩盖概率的概率.最大的概率估计估计.下一个分发功能.

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
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

248

相关实验视频

Last Updated: Jun 22, 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
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

248

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:

  • 竞争的风险数据往往带来挑战,缺少故障类型信息.
  • 准确的统计方法对于分析不确定数据的健康结果至关重要.

研究的目的:

  • 开发和评估对当前状态数据的参数和非参数估计方法,其中有两个竞争的风险和缺失的故障类型.
  • 为了解决依赖时间的缺失概率,这些概率取决于故障时间,监控时间和真故障类型.

主要方法:

  • 用于参数和非参数方法的最大概率估计.
  • 研究了开发的估计器的非对称性质.
  • 进行模拟研究以评估有限样本的性能.

主要成果:

  • 开发了新的统计方法来处理竞争风险中缺失的故障类型.
  • 通过模拟来证明拟议的估计器的有效性.
  • 缺失的机制被证明是不可忽视的,因为时间依赖的概率.

结论:

  • 拟议的方法提供了一个强大的框架来分析与缺失故障类型的竞争风险数据.
  • 该研究为健康研究中的生物统计分析提供了有价值的工具,以听力损失数据为例.