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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
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

710
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...
710
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

224
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
224

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

Unified estimation for Cox regression model with nonmonotone missing at random covariates.

Statistics in medicine·2022
Same author

Weighted generalized estimating equations and unified estimation for longitudinal data with nonmonotone missing data patterns.

Statistics in medicine·2021
Same author

Statistical inference for missing data mechanisms.

Statistics in medicine·2020
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 <math><semantics><mi>Δ</mi> <annotation>$\Delta$</annotation></semantics></math> Mutant Data.

Biometrical journal. Biometrische Zeitschrift·2026
查看所有相关文章

相关实验视频

Updated: Sep 10, 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

3.4K

在随机数据缺失的部分线性模型的统一估计方法

Yang Zhao1

  • 1Department of Mathematics and Statistics, University of Regina, Regina, Saskatchewan, Canada.

Biometrical journal. Biometrische Zeitschrift
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于估计缺少数据的部分线性模型的新方法. 这种方法提高了估计效率,并且即使缺乏复杂的数据模式,也具有稳定性.

关键词:
随机丢失的不单调的缺失模式部分线性工作模型权重局部线性核方法

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
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

相关实验视频

Last Updated: Sep 10, 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

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
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

科学领域:

  • 统计数据
  • 生物统计学
  • 流行病学

背景情况:

  • 在具有混变量的观测研究中,部分线性模型对于因果推断至关重要.
  • 现有的方法在响应,治疗和混因素方面缺乏数据.
  • 稳定性和无异常分布性质是因果无效假设测试的关键.

研究的目的:

  • 开发和评估部分线性模型的估计方法,在随机数据中缺少非单调数据.
  • 与标准完整案例方法相比,提高估计效率.
  • 为复杂的缺失数据场景提供计算上简单且可实现的解决方案.

主要方法:

  • 开发了一种使用部分线性工作模型的一般估计方法.
  • 建议对非对称差异进行引导估计.
  • 为缺少数据概率推的半参数模型.

主要成果:

  • 建议的估计器是一致的,独立于工作模型的正确性.
  • 与完整案例方法相比,估计效率提高.
  • 在标准软件中展示了计算简单性和可实现性.

结论:

  • 新方法有效处理部分线性模型中随机缺失的非单调数据.
  • 为因果推理提供了强大而有效的方法.
  • 通过模拟研究和现实数据示例进行验证.