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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

41
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...
41
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

430
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
430
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

73
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
73
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

256
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
256
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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
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...
54

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在健康经济评估中建模依赖参数的简化方法:一个教程

Xuanqian Xie1, Alexis K Schaink2, Sichen Liu3

  • 1Health Technology Assessment Program, Ontario Health, 525 University Avenue, 5th Floor, Toronto, ON, M5G 2L3, Canada. shawn.xie@ontariohealth.ca.

Applied health economics and health policy
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PubMed
概括

本教程简化了在健康经济模型中处理依赖参数的方法. 它介绍了模拟多变量正常数据和估计过渡概率的可访问方法,有助于复杂模型的开发.

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科学领域:

  • 卫生经济学 卫生经济学
  • 生物统计学 生物统计学
  • 数学建模的数学建模

背景情况:

  • 卫生经济评估中的模型参数经常相互依赖.
  • 对于健康经济学家来说,模拟多变量正常 (MVN) 数据和估计在竞争风险下马尔科夫模型过渡概率的现有方法是复杂的.
  • 这项工作解决了在健康经济建模中管理依赖参数的可访问技术的需求.

研究的目的:

  • 在健康经济建模中提供易于实施的方法来处理依赖参数.
  • 用SAS和R的实例和代码来说明这些方法.
  • 扩展常用的技术,使其具有更广泛的应用.

主要方法:

  • 介绍了健康经济模型中依赖参数处理的分析证明和简化方法.
  • 证明从总结统计数据中量化协差和相关系数.
  • 描绘了生成MVN分布数据和使用单变量正常分布数据用于人口异质性.
  • 介绍了一种条件概率方法,用于单个马尔科夫模型周期内的多个状态过渡.

主要成果:

  • 通过总结统计数据成功量化协差和相关系数.
  • 展示了MVN数据生成与医生访问和成本数据的例子.
  • 通过回归模型有效地使用单变量正常分布数据来捕捉人口异质性.
  • 在马尔科夫模型中的单向和双向状态转换中应用条件概率方法.

结论:

  • 建议扩展用于处理依赖参数的标准方法.
  • 为各种各样的统计专业知识的健康经济建模者提供简化,易于应用的方法.
  • 通过改进的参数处理,促进更强大,更准确的健康经济评估.