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

370
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...
370
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

315
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...
315
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

99
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.
99
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

403
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
403
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

185
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...
185

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

Two-stage sampling for better survival model performance.

BMC medical research methodology·2025
Same author

Adjusted predictions for generalized estimating equations.

Biometrics·2025
Same author

Endoplasmic reticulum protein 5 attenuates platelet endoplasmic reticulum stress and secretion in a mouse model.

Blood advances·2022
Same author

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.

GigaScience·2022
Same author

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.

NPJ digital medicine·2022
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 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

使用考克斯模型进行强大的变量选择方法 - - 选择性的实践基准研究.

Yunwei Zhang1,2,3, Samuel Muller2,3

  • 1School of Mathematics, Statistics, Chemistry and Physics, Murdoch University, 90 South St, Murdoch WA 6150, Australia.

Briefings in bioinformatics
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

强大的考克斯模型通过高维的奥米克和生存数据优于变量选择的非强大的方法. 它们在异常值的情况下提供了卓越的性能,在它们不存在时保持了准确性和效率.

关键词:
考克斯模型 考克斯模型考克斯模型受到惩罚强大的变量选择选择.生存分析,生存分析.选择变量的选择变量.

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
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

相关实验视频

Last Updated: Jun 10, 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
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

科学领域:

  • 生物统计学 生物统计学
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 与受审查的生存信息相结合的高维的奥米克数据提出了变量选择的挑战.
  • 扭曲的生存时间分布需要强大的统计方法.
  • 将强大的方法扩展到生存模型是一个活跃的研究领域.

研究的目的:

  • 为了比较坚固和非坚固的Cox模型的可变选择性能.
  • 评估异常值对生存分析中变量选择的影响.
  • 为omics数据中的变量选择提供实际建议.

主要方法:

  • 选择性审查和经验性比较12个强和非强的Cox模型.
  • 分析高维的奥米克数据与受审查的生存结果.
  • 在不同的条件下评估可变选择性能,包括异常值的存在.

主要成果:

  • 强大的考克斯模型与非强大的模型相比,显示出更高的变量选择性能,特别是在异常值的存在时.
  • 共变量和建模方法的微小变化显著影响方法性能.
  • 强大的方法保持良好的效率和准确性,即使异常值不存在.

结论:

  • 强大的考克斯模型被推用于实用的变量选择,使用高维的欧米和审查的生存数据.
  • 这些模型为处理异常值提供了可靠的方法,提高了变量选择的准确性.
  • 这项研究强调了考虑可靠的方法的重要性,以改善对复杂生物数据的洞察力.