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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

182
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,...
182
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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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

570
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...
570
Multiple Regression01:25

Multiple Regression

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

93
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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混合多层向量自回归建模.

Anja F Ernst1, Marieke E Timmerman1, Feng Ji2

  • 1Department Psychometrics and Statistics, University of Groningen.

Psychological methods
|August 10, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的混合多层向量自回归模型,用于在密集的纵向数据中识别特征和动态过程中的明显个体差异. 该模型成功地在COGITO研究中的情感数据中识别了三个组件.

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科学领域:

  • 心理学 心理学 心理学
  • 统计 统计 统计 统计
  • 纵向数据分析 纵向数据分析

背景情况:

  • 密集的纵向研究正在增长,需要捕捉个人差异的模型.
  • 现有的多层向量自回归模型可以扩展,以考虑动态过程中的异质性.

研究的目的:

  • 引入和验证混合物多层向量自回归模型.
  • 在纵向数据中识别具有相似特征和动态过程的独特子组.
  • 通过共同建模不同年龄组,分析COGITO研究中的异质情感数据.

主要方法:

  • 混合物多层向量自回归建模的开发.
  • 模拟研究以验证模型性能并检查预测器集中.
  • 适用于来自COGITO研究的情感数据,该研究涉及200多名参与者,测量时间超过100天.

主要成果:

  • 拟议的模型成功地确定了三种不同的混合物成分.
  • 这些组件代表了在平均值,自行回归和交叉回归中具有相似性的个体.
  • 对COGITO研究数据的分析表明,该模型能够联合分析异质样本.

结论:

  • 混合多层向量自回归建模对于在密集的纵向数据中识别子组是有效的.
  • 该模型提供了对心理特征和动态的个体差异的见解.
  • 已识别的组件为不同年龄组的情感过程提供了基于发育心理学的解释.