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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

92
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,...
92
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Clearance Models: Noncompartmental Models

37
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...
37
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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...
56
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

381
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...
381
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
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...
26

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相关实验视频

Updated: Jun 2, 2025

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

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Published on: September 16, 2022

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高维部分线性功能考克斯模型

Xin Chen1,2, Hua Liu3, Jiaqi Men4

  • 1School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China.

Biometrics
|January 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的高维部分线性功能考克斯模型,用于分析具有非线性功能预测效应的生存数据. 该模型在诸如脏移植存活率等场景中提高了时间到事件分析的准确性.

关键词:
在B-spline上使用.功能性主要组件分析分析长期生存分析非线性效应是一种非线性效应.

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科学领域:

  • 生物统计学 生物统计学
  • 生存分析的分析.
  • 功能数据分析 功能数据分析

背景情况:

  • 传统的功能性考克斯模型假设功能主要组件 (FPC) 评分和危险率之间的线性关系.
  • 这种线性假设在现实应用中经常被违反,例如分析移植接受者的生存时间.
  • 现有的方法可能无法充分捕捉功能预测因子对时间到事件结果的复杂,非线性影响.

研究的目的:

  • 开发和验证一个高维部分线性功能Cox模型.
  • 在时间到事件数据分析中适应功能预测器的非线性效应.
  • 通过结合功能和标量预测器来改善生存时间预测.

主要方法:

  • 介绍一个高维的部分线性功能考克斯模型.
  • 应用组平滑剪切绝对偏差 (SCAD) 方法用于标量预测器和FPC的变量选择.
  • 使用B-splines来估计功能预测者的非线性影响.

主要成果:

  • 模拟研究证明了拟议模型估计的有限样本性能.
  • 该模型成功地确定了与移植受体的长期存活相关的显著标量预测因子.
  • 关于功能预测因子对患者危险率的非线性影响,进行了推断.

结论:

  • 提出的高维部分线性功能考克斯模型有效地处理在生存分析中的非线性功能预测效应.
  • 这种方法为传统的功能Cox模型提供了更灵活,更准确的替代方案.
  • 该模型为影响生存时间的因素提供了宝贵的见解,特别是在复杂的医疗数据集中,如脏移植.