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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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

Parametric Survival Analysis: Weibull and Exponential Methods

413
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...
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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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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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glmmPen:高维的惩罚性通用化的线性混合模型

Hillary M Heiling1, Naim U Rashid1, Quefeng Li1

  • 1University of North Carolina Chapel Hill.

The R journal
|May 31, 2024
PubMed
概括
此摘要是机器生成的。

glmmPen R包允许在高维通用线性混合模型 (GLMM) 中同时选择固定和随机效应. 这种方法克服了传统方法的局限性,提高了复杂数据集的模型准确性.

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科学领域:

  • 统计 统计 统计 统计
  • 计算生物学 计算生物学
  • 生物统计学 生物统计学

背景情况:

  • 一般化的线性混合模型 (GLMMs) 对于分析相关的非高斯数据至关重要.
  • 准确选择固定和随机效应至关重要,以防止GLMM中的偏差.
  • 以前的联合效应选择方法仅限于较低维度的问题.

研究的目的:

  • 介绍R包glmmPen用于高维的GLMM.
  • 为联合固定和随机效应选择开发一个处罚模型框架.
  • 为参数估计提供一个高效的计算算法.

主要方法:

  • 使用处罚的通用线性混合模型框架.
  • 使用蒙特卡洛预期条件最小化 (MCECM) 算法进行参数估计.
  • 在glmmPen包中利用Stan和RcppArmadillo提高计算效率.

主要成果:

  • glmmPen软件包有助于在高维的GLMM中联合选择固定和随机效果.
  • 在MCECM算法提供高效的参数估计.
  • 模拟显示在选择固定和随机效应方面表现良好.

结论:

  • glmmPen为高维的GLMM提供了一个新的解决方案,解决了效果选择的局限性.
  • 该包支持双项式,高斯式和波松式家族,具有各种惩罚功能.
  • 这种方法提高了GLMM分析在复杂研究领域的准确性和可靠性.