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Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

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...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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Related Experiment Video

Updated: Jul 14, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

New models for old questions: generalized linear models for cost prediction.

John L Moran1, Patricia J Solomon, Aaron R Peisach

  • 1Department of Intensive Care Medicine, The Queen Elizabeth Hospital, Woodville, South Australia, Australia. john.moran@nwahs.sa.gov.au

Journal of Evaluation in Clinical Practice
|May 24, 2007
PubMed
Summary

Generalized linear models (GLMs) offer an alternative to ordinary least squares regression (OLS) for predicting intensive care unit (ICU) patient costs. GLMs provide a more accurate model of cost data error structures.

Related Experiment Videos

Last Updated: Jul 14, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Health Economics
  • Biostatistics
  • Critical Care Medicine

Background:

  • Generalized linear models (GLMs) are increasingly used for cost data analysis.
  • GLMs are extensions of linear regression, incorporating specific response distributions and link functions.
  • They model the relationship between a response mean and additive model effects.

Purpose of the Study:

  • Compare GLMs and ordinary least squares regression (OLS) for predicting individual patient costs in adult intensive care units (ICUs).
  • Evaluate the utility of the inverse Gaussian distribution family within GLMs for cost prediction.
  • Assess model performance using prediction error metrics and residual analysis.

Main Methods:

  • Prospective costing study in three university-affiliated adult ICUs over six months (1991).
  • Data collected on ICU utilization, patient demographics, and admission details for consecutive admissions.
  • Model performance evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Main Results:

  • The study included 1098 adult ICU survivors with a mean age of 56 years; 41% were female.
  • Mean ICU cost per episode was A$6311 (SD A$9689).
  • OLS with log cost transformation and GLM with Gaussian family/log link showed minimal prediction error (MAE ~4780-4798, RMSE ~8907-8965).

Conclusions:

  • Appropriately specified GLMs can supplement traditional OLS cost models.
  • GLMs offer advantages in modeling the error structure of cost data.
  • The inverse Gaussian family with a log link is a viable option within GLMs for ICU cost prediction.