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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

38
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
38
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

51
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
51
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
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
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

482
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...
482
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

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

The pigment-dispersing factor receptor (PDFR) gene is involved in circadian rhythm and moulting in Hyphantria cunea.

Insect molecular biology·2026
Same author

Overexpression of <i>PtrPIP2:4</i> Accelerates Adventitious Root Emergence, Promotes Adventitious Root Elongation, and Increases Lateral Root Number in Poplar.

Plants (Basel, Switzerland)·2026
Same author

Preparation methods, structural characterization, pharmacological properties, and potential industrial utilization of polysaccharides from Aloe vera: A review.

International journal of biological macromolecules·2026
Same author

Longitudinal Association of Quantitative Background Parenchymal Enhancement with Breast Cancer Risk Among Women with BRCA1/2 Pathogenic Variants.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Lesion detectability and masking disparity assessment in breast tomosynthesis across diverse populations using in-silico imaging trials.

IEEE transactions on medical imaging·2026
Same author

Systemic inflammation as a mediator between food preferences and metabolic syndrome: a cross-sectional study.

Frontiers in nutrition·2026

相关实验视频

Updated: Jun 26, 2025

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

一种受约束的最大概率方法,用于开发精确校准的模型来预测二进制结果.

Yaqi Cao1,2, Weidong Ma2, Ge Zhao3

  • 1Department of Statistics, School of Science, Minzu University of China, Beijing, China.

Lifetime data analysis
|May 8, 2024
PubMed
概括
此摘要是机器生成的。

评估新的风险因素需要公正的模型. 本研究引入了一种半参数方法来校准模型,确保对候选预测因子的公平评估,即使使用非代表性样本.

关键词:
校准 校准 校准 校准 校准 校准有限制的最大概率估计.后勤回归的逻辑回归风险预测风险预测

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

相关实验视频

Last Updated: Jun 26, 2025

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
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 医疗信息学 医疗信息学

背景情况:

  • 在风险建模中评估候选预测因素通常涉及将模型性能与预测因素进行比较.
  • 这种比较只有在两个模型的风险估计在目标人群中不偏的情况下才有效.
  • 非代表性便利样本通常为候选预测器提供数据,可能导致偏见的风险估计和不公平的评估.

研究的目的:

  • 为模型拟合提出一个半参数方法,以确保良好的校准,使候选预测器的公正评估.
  • 解决使用非代表性样本来评估风险建模中预测因素的附加值的挑战.
  • 为了克服需要代表性样本来准确评估模型改进的实际局限性.

主要方法:

  • 开发了一种半参数方法,该方法对与精确校准的基本模型对应的装配模型进行校准.
  • 强制校准通过在概率函数的最大化过程中施加约束.
  • 研究了模型参数估计的理论性质,并进行了广泛的模拟研究.

主要成果:

  • 拟议的方法在模拟研究中证明了改进的模型校准.
  • 理论分析支持了模型参数估计的属性.
  • 该方法允许在没有代表性样本的情况下对候选预测者的附加值进行公正的评估.

结论:

  • 开发的半参数方法为在风险建模中评估候选预测因子提供了强大的解决方案,即使使用方便样本.
  • 这种方法确保了不偏见的风险估计和对模型改进的公平评估.
  • 应用于乳腺癌风险评估,它强调了高加索女性乳腺密度的附加值.