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

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Data Validation01:03

Data Validation

5.3K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.3K
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
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

664
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
664
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

712
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...
712
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

258
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
258

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

Variable selection for clinical prediction models in low-dimensional data - a simulation study comparing traditional regression and machine learning methods.

BMC medical research methodology·2026
Same author

Neutrophil Extracellular Traps in Patients with Intracerebral Hemorrhage.

Neurocritical care·2026
Same author

Effects of a clinical metagenomics intervention on clinical outcomes, healthcare costs, and health-related quality of life in patients with sepsis or septic shock: results of the randomized-controlled DigiSep trial.

Intensive care medicine·2026
Same author

Predictive Value of Routine Imaging for the Diagnosis of Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

Annals of surgical oncology·2026
Same author

ASO Visual Abstract: Predictive Value of Routine Imaging for the Diagnosis of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

Annals of surgical oncology·2026
Same author

Estimation, testing and sample size calculation within the responder-stratified exponential survival model.

Journal of biopharmaceutical statistics·2026

相关实验视频

Updated: Sep 13, 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

2.2K

在临床预测建模中将多重归算与内部模型验证相结合:一个系统的方法论审查.

Sinclair Awounvo1, Meinhard Kieser1, Manuel Feißt1

  • 1Institute of Medical Biometry, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany.

Journal of clinical epidemiology
|August 3, 2025
PubMed
概括

由于简单性,大多数临床预测建模研究在内部模型验证 (IMV) 之前使用多重归算 (MI). 在IMV期间的MI更复杂,但对于具有更高缺失数据率的大型研究来说可能会带来好处.

关键词:
在 Bootstrap 中使用 Bootstrap.计入计算是指计入计算的方法.我们的老鼠是老鼠.建模建模模型是什么预测 预测 预测验证 验证 验证 验证

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

673
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

相关实验视频

Last Updated: Sep 13, 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

2.2K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

673
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

科学领域:

  • 临床预测建模临床预测建模
  • 统计方法学的统计方法.
  • 数据科学是数据科学.

背景情况:

  • 缺少数据是临床研究中的一个重大挑战,影响了预测模型的可靠性.
  • 多重归算 (MI) 和内部模型验证 (IMV) 是解决缺失数据和确保模型稳定性的关键技术.
  • 在临床预测建模 (CPM) 中,MI和IMV策略的最佳组合存在有限的指导.

研究的目的:

  • 调查当前在CPM研究中将MI与IMV结合在一起的做法.
  • 确定平衡预测性能,方法复杂性和资源需求的挑战.
  • 分析用于MI和IMV集成的不同策略.

主要方法:

  • 使用MI和验证进行CPM研究的PubMed,Web of Science和MathSciNet的系统文献搜索.
  • 基于MI相对于IMV的时间进行的研究的分类:MI-IMV前与MI-IMV期间.
  • 基于研究关键参数的策略选择描述,包括MI方法和IMV技术.

主要成果:

  • 包括108项研究,其中85%使用MI-prior-IMV.
  • 在MI-during-IMV研究中,样本规模更大,数据丢失率更高.
  • 通过链式方程 (MICE) 进行多重归算是主要的MI方法;引导和交叉验证是MI-during-IMV中常见的IMV技术.

结论:

  • 由于其实施简单,MI-prior-IMV是普遍存在的.
  • 虽然MI-during-IMV更复杂,但与更大的数据集和更高的失踪率有关.
  • 需要进一步的研究来系统地评估权衡,并提供关于最佳MI-IMV策略的指导.