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相关概念视频

An R-Based Landscape Validation of a Competing Risk Model05:37

An R-Based Landscape Validation of a Competing Risk Model

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The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index06:55

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When randomized controlled trials are not feasible, a comprehensive health care data source like the Military Health System Data Repository provides an attractive alternative for retrospective analyses. Incorporating mortality data from the national death index and balancing differences between groups using propensity weighting helps reduce biases inherent in retrospective...
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Probability Laws01:49

Probability Laws

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Overview
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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相关实验视频

Updated: Jan 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
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使用连续排序概率得分验证基于生理学上的药理动力学模型:超出正确的平均值.

Laurens Sluijterman1, Marjolein van Borselen2, Rick Greupink2

  • 1IQ Health, Section Biostatistics, Radboud University Medical Center, Nijmegen, the Netherlands.

CPT: pharmacometrics & systems pharmacology
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的验证方法,用于使用连续排列概率得分 (CRPS) 的生理基础药理动力学 (PBPK) 模型. 这种方法对药物开发更严格地量化模型性能.

关键词:
连续排名的概率得分连续排名的概率得分.评估框架 评估框架基于生理学的药物动力学模型.验证验证的时间

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Model Approaches for Pharmacokinetic Data: Physiological Models
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Model Approaches for Pharmacokinetic Data: Physiological Models

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相关实验视频

Last Updated: Jan 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Model Approaches for Pharmacokinetic Data: Physiological Models
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Model Approaches for Pharmacokinetic Data: Physiological Models

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科学领域:

  • 药理动力学和药理动力学
  • 计算生物学和生物信息学
  • 药物开发和药理学 药物开发和药理学

背景情况:

  • 基于生理学的药理动力学 (PBPK) 模型在基于模型的药物开发 (MIDD) 中至关重要.
  • 现有的模型评估指南缺乏具体的量化方法来验证.
  • 需要精确的指标来评估PBPK模型的预测准确性.

研究的目的:

  • 为PBPK模型提出严格的验证方法,使用连续排列概率得分 (CRPS).
  • 在PBPK建模中证明CRPS对个人和人口水平预测的适用性.
  • 引入单一PBPK模型验证的技能得分方法.

主要方法:

  • 连续排列概率得分 (CRPS) 的应用用于PBPK模型验证.
  • 使用拟议的基于CRPS的验证技术对两个PBPK模型进行比较.
  • 开发一个可访问的在线工具来实施CRPS验证方法.

主要成果:

  • CRPS有效量化了PBPK模型如何重现观察到的数据分布.
  • 通过CRPS,可以比较竞争的PBPK模型之间的预测性能.
  • 使用CRPS的技能得分方法促进了单一模型的验证.

结论:

  • 拟议的基于CRPS的验证方法提供了对PBPK模型的更彻底和定量评估.
  • 这个指标是多功能,适用于个人和人口预测,以及可比的模型.
  • 在药物开发和其他建模应用中,CRPS指标和相关工具可以提高PBPK模型的可靠性.