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

Survival Tree01:19

Survival Tree

166
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
166
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
152
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

634
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...
634
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

200
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.
200
Random Variables01:09

Random Variables

13.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.4K

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

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same author

Nivolumab Plus Ipilimumab in Patients With Solid Tumors With High Tumor Mutation Burden: Results From the Targeted Agent and Profiling Utilization Registry Study.

JCO precision oncology·2026
Same author

Pertuzumab and Trastuzumab in Patients With <i>ERBB2/3</i>-Altered Urothelial or Ovary/Fallopian Tube Cancer: Results From the Targeted Agent and Profiling Utilization Registry Study.

JCO precision oncology·2026
Same author

Temsirolimus in Patients With Solid Tumors With <i>PIK3CA</i> Mutations: Results From the Targeted Agent and Profiling Utilization Registry (TAPUR) Study.

JCO precision oncology·2026
Same author

Cytoplasmic versus nuclear localization of androgen receptor splice variant 7 as a predictor of benefit from androgen receptor pathway inhibitors in metastatic castration-resistant prostate cancer (PROPHECY trial).

Prostate cancer and prostatic diseases·2026
Same author

Trial Design and Objectives for Patients With Prostate Cancer: Recommendations From the Prostate Cancer Working Group 4.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 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

10.3K

通过规范化的稀缺输入神经网络对多变量失效时间数据进行变量选择.

Bin Luo1, Susan Halabi2

  • 1School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA.

Bioengineering (Basel, Switzerland)
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种分析多个相关生存结果的新方法,改善临床试验中的变量选择和预测准确性. 该方法有效地识别了共享的预测因子,以便更好地进行预后建模.

关键词:
拉索 (Lasso) 团队的拉索 (Lasso) 是一个小组.具有高维度的高维度.多变量失效时间不凸的惩罚不是凸的惩罚.选择变量的选择变量.

更多相关视频

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

相关实验视频

Last Updated: Sep 18, 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

10.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

科学领域:

  • 生物统计学 生物统计学
  • 临床试验 临床试验
  • 基因组学就是基因组学.

背景情况:

  • 在临床研究中,分析与相关终点相关的多变量失效时间数据具有挑战性.
  • 同时选择变量和模型估计对于确定预后因素至关重要.

研究的目的:

  • 开发一个统一的框架,用于识别跨多个时间到事件结果的共享预测因素.
  • 在低维和高维设置中增强变量选择和预测性能.

主要方法:

  • 一种对线性边际危险模型的惩罚性伪部分概率方法,带有组 LASSO 类型的惩罚.
  • 扩展到稀疏输入神经网络模型,对非线性效应进行结构化群体惩罚.
  • 使用复合梯度下降算法进行优化.

主要成果:

  • 拟议的方法显示出优越的变量选择和比传统方法更好的预测性能.
  • 该框架对违反常见预测假设的情况表现出强度.
  • 在前列腺癌数据中确定了既定和新的预后单核酸多态.

结论:

  • 统一框架为复杂的多变量生存数据分析提供了一个灵活而强大的工具.
  • 在预后建模和个性化医学中的潜在实用性.
  • 方便识别共享的预测因子,以改善临床试验分析.