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相关概念视频

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

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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...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
551
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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

722
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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用集成嵌套拉普拉斯近似来处理缺失数据和测量误差的联合贝叶斯框架.

Emma Skarstein1, Sara Martino1, Stefanie Muff1,2

  • 1Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.

Biometrical journal. Biometrische Zeitschrift
|September 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一个统一的贝叶斯框架,同时解决测量误差 (ME) 和回归共变量中缺失的数据. 该方法利用集成嵌套拉普拉斯近似 (INLA) 进行可靠的数据分析.

关键词:
贝叶斯联合模型是贝叶斯联合模型.伯克森测量误差 测量误差经典的测量误差是传统的测量误差.集成嵌套拉普拉斯近似方法缺失的数据 缺失的数据

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科学领域:

  • 统计建模 统计建模
  • 数据分析方法论数据分析方法论

背景情况:

  • 测量误差 (ME) 和缺失数据是统计分析中常见的挑战.
  • 现有的方法通常将ME和缺失数据视为单独的问题,尽管它们有理论上的联系.
  • 在回归共变量中考虑ME并不像处理缺失数据那么常见.

研究的目的:

  • 开发一个统一的贝叶斯框架,同时处理ME和连续共变量的缺失数据.
  • 扩展现有的ME方法,将缺失的数据纳入ME的极端病例.
  • 提供适用于各种ME类型 (经典,伯克森) 和回归模型中的缺失数据场景的灵活方法.

主要方法:

  • 使用贝叶斯框架与集成嵌套拉普拉斯近似 (INLA).
  • 利用缺少数据与经典医学理论之间的关系.
  • 开发INLA内部处理缺失数据的方法,适用于当没有ME存在时.
  • 将Berkson ME纳入同一个贝叶斯框架.

主要成果:

  • 在同一个共变量中,证明了对ME和缺失数据的同时会计.
  • 展示了一种处理INLA中缺少数据的方法,作为ME的特殊案例.
  • 扩大了框架,包括伯克森 ME.
  • 联合贝叶斯框架容纳了ME和缺失数据在连续共变量中的组合.

结论:

  • 拟议的联合贝叶斯框架为ME和回归模型中缺少的数据提供了统一的解决方案.
  • 该方法是多功能性的,处理经典ME,伯克森ME和缺失数据,单独或组合.
  • 该方法以模拟和真实数据为例,并以可重复的R-INLA和实验室实例为支持.