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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
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...
69
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

39
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...
39
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

57
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
57
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

93
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
93
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

62
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
62
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

123
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...
123

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

Prevalence of multiple human intestinal parasites across diverse environments in Madagascar.

PLoS neglected tropical diseases·2026
Same authorSame journal

A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same author

Estimate an Exposure Response Function with Negative Controls: A Bayesian Nonparametric Approach.

American journal of epidemiology·2026
Same author

baysc: An R package for Bayesian survey clustering.

Journal of open source software·2026
Same author

A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures.

Current environmental health reports·2026
Same author

A social-ecological trap theory-informed investigation of dietary patterns in southwestern Madagascar.

Frontiers in nutrition·2026

相关实验视频

Updated: Jun 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K

从调查数据中识别饮食消费模式:贝叶斯的非参数隐性类模型.

Briana J K Stephenson1, Stephanie M Wu1, Francesca Dominici1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|April 15, 2024
PubMed
概括

这项研究引入了一种新的贝叶斯模型,用于在国家调查中准确识别饮食模式,即使采用不成比例的子组抽样. 该方法提高了对不同人群的饮食习惯评估的普遍性.

科学领域:

  • 营养流行病学 营养流行病学
  • 统计建模 统计建模
  • 公共卫生研究 公共卫生研究

背景情况:

  • 国家调查中的饮食评估提供了人口层面的见解,但由于分组抽样不成比例,因此面临普遍性挑战.
  • 了解真正的饮食模式对于公共卫生干预至关重要,但标准方法可能无法完全解释复杂的调查设计.

研究的目的:

  • 开发和验证贝叶斯超拟合潜伏类模型,从国家调查数据中推导出强大的饮食模式.
  • 提高饮食模式分析的可识别性和通用性,特别是针对社会经济弱势群体.

主要方法:

  • 提出了一种新的贝叶斯超拟合隐性类型模型,该模型包含了调查设计和采样变化.
  • 该模型的性能通过模拟进行了评估,比较了其真实人口模式和流行率与标准方法的识别能力.
  • 该模型被应用用于确定贫困收入水平的130%或以下的成年人中饮食摄入模式.

主要成果:

  • 建议的贝叶斯模型表明,与标准方法相比,真实人口饮食模式的识别能力和在模拟研究中的流行率得到了改善.
  • 在生活在130%贫困收入水平或以下的成年人中,确定了五种不同的饮食模式.
  • 该研究提供了可重现的代码和数据,以促进进一步的饮食模式分析研究.

结论:

关键词:
贝叶斯的非参数.尼汉斯 (NHANES) 是一个名人.饮食模式 饮食模式隐藏类模型模型中的隐藏类模型.调查设计调查设计调查设计

更多相关视频

Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents
09:10

Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents

Published on: November 1, 2019

10.7K
Author Spotlight: Accessible M&M-Based Mouse Model for Investigating Binge Eating Disorder - Insights into Eating Behaviors, Anxiety, and Neural Mechanisms
05:15

Author Spotlight: Accessible M&M-Based Mouse Model for Investigating Binge Eating Disorder - Insights into Eating Behaviors, Anxiety, and Neural Mechanisms

Published on: January 10, 2025

804

相关实验视频

Last Updated: Jun 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K
Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents
09:10

Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents

Published on: November 1, 2019

10.7K
Author Spotlight: Accessible M&M-Based Mouse Model for Investigating Binge Eating Disorder - Insights into Eating Behaviors, Anxiety, and Neural Mechanisms
05:15

Author Spotlight: Accessible M&M-Based Mouse Model for Investigating Binge Eating Disorder - Insights into Eating Behaviors, Anxiety, and Neural Mechanisms

Published on: January 10, 2025

804
  • 开发的贝叶斯模型为从复杂的国家调查数据中识别饮食模式提供了更准确和更可概括的方法.
  • 这种方法提高了对弱势群体饮食习惯的理解,为有针对性的公共卫生战略铺平了道路.
  • 可再生资源的可用性鼓励更广泛地采用和进一步研究饮食模式分析.