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Related Concept Videos

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.
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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

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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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Updated: Jul 5, 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

Marginal structural models for partial exposure regimes.

Stijn Vansteelandt1, Karl Mertens, Carl Suetens

  • 1Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium. stijn.vansteelandt@ugent.be

Biostatistics (Oxford, England)
|May 27, 2008
PubMed
Summary
This summary is machine-generated.

Hospital-acquired infections in intensive care units (ICUs) increase mortality risk. This study introduces new methods to accurately quantify this risk, accounting for complex patient factors and treatments.

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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:

  • Critical Care Medicine
  • Epidemiology
  • Biostatistics

Background:

  • Intensive care unit (ICU) patients face high risks of hospital-acquired infections (HAIs).
  • The impact of HAIs on mortality in ICUs is not well understood.
  • Standard statistical methods struggle with time-dependent confounders in ICU settings.

Purpose of the Study:

  • To quantify the effect of HAIs on mortality in Belgian ICUs.
  • To address limitations of standard statistical analyses and existing marginal structural models.
  • To develop a novel statistical approach for analyzing infection effects in critically ill patients.

Main Methods:

  • Utilized data from the National Surveillance Study of Nosocomial Infections in ICUs (Belgium).
  • Introduced a new class of marginal structural models for partial exposure regimes.
  • Accounted for time-dependent confounders and informative censoring due to hospital discharge.

Main Results:

  • Developed a method to estimate the hazard of death associated with acquiring infection on a specific ICU day.
  • Addressed the ill-defined nature of 'never infected' in models when discharge precedes potential infection.
  • Provided a more robust framework for analyzing infection's impact on ICU mortality.

Conclusions:

  • The proposed partial exposure marginal structural models offer a more accurate way to assess HAI mortality risks in ICUs.
  • This methodology overcomes key challenges posed by time-dependent confounding and informative censoring.
  • Accurate quantification of HAI impact is crucial for improving patient outcomes in critical care.