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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
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...
127
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

717
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...
717
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

537
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
537
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

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相关实验视频

Updated: Sep 13, 2025

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

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使用mgcvv计算数据的灵活分布式滞后模型.

Theo Economou1,2, Daphne Parliari3, Aurelio Tobias4

  • 1Department of Mathematics and Statistics, University of Exeter, Exeter, UK.

The American statistician
|August 1, 2025
PubMed
概括
此摘要是机器生成的。

本教程介绍了使用R包mgcv的分布式滞后非线性模型 (DLNMs) 的灵活实现. 它可以通过近似贝叶斯推理来定量不确定性和模型检查,用于流行病学数据分析.

关键词:
贝叶斯的推理 贝叶斯的推理DLNM DLNM DLNM DLNM DLNM 在线环境流行病学环境流行病学热应力是一种热应力.处罚的分线线是受到惩罚的.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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相关实验视频

Last Updated: Sep 13, 2025

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

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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ScanLag: High-throughput Quantification of Colony Growth and Lag Time
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ScanLag: High-throughput Quantification of Colony Growth and Lag Time

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科学领域:

  • 环境流行病学环境流行病学
  • 生物统计学 生物统计学
  • 统计建模 统计建模

背景情况:

  • 分布式滞后非线性模型 (DLNMs) 对于分析环境暴露和健康结果至关重要.
  • 灵活的实施和强大的模型检查对于可靠的流行病学研究至关重要.
  • R包mgcv为高级统计建模提供了强大的工具.

研究的目的:

  • 通过使用R包mgcv来证明DLNM的灵活实施.
  • 展示不确定性量化的方法和全面的模型检查.
  • 用现实世界的数据来说明DLNM在流行病学研究中的应用.

主要方法:

  • 在R中使用mgcv包来实现DLNM.
  • 采用近似贝叶斯推理,通过将光滑线条解释为随机量.
  • 纳入时间结构,异常值的混合分布,共变相互作用和空间组件 (光滑可变性,马尔科夫随机场,等级配方).

主要成果:

  • 在R.中展示了灵活的DLNM实施.
  • 展示了不确定性量化和模型检查能力.
  • 插图处理时间结构,异常值,共同变量相互作用和空间依赖.

结论:

  • R包mgcv为实施DLNM提供了一个灵活的框架.
  • 大致贝叶斯推理促进了可靠的不确定性量化和模型验证.
  • 这些方法适用于各种结构的复杂流行病学数据.