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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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,...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...

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Related Experiment Video

Updated: May 7, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity.

Martijn Lappenschaar1, Arjen Hommersom, Peter J F Lucas

  • 1Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.

Journal of Clinical Epidemiology
|September 17, 2013
PubMed
Summary

Multimorbidity, the co-occurrence of multiple chronic conditions, progresses rapidly. Health risk factors like hypertension and lipid disorders significantly increase the incidence of new and combined cardiovascular disorders.

Keywords:
Bayesian networksCardiovascular diseaseInterpractice variationMultilevel analysisMultimorbiditySynergy

Related Experiment Videos

Last Updated: May 7, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Traditional epidemiologic techniques struggle to analyze the joint progression of multiple chronic diseases.
  • Understanding multimorbidity dynamics is crucial for effective healthcare management.

Purpose of the Study:

  • To investigate the simultaneous progression of six chronic cardiovascular conditions using multilevel temporal Bayesian networks.
  • To gain new clinical insights into the complex interplay of multimorbidity over time.

Main Methods:

  • Analysis of 1.5 million patient-years from 90 general practice registries in the Netherlands.
  • Utilized multilevel temporal Bayesian networks to model disease progression.
  • Corrected for patient- and practice-related variables.

Main Results:

  • Cumulative incidence of new morbidities increases substantially with baseline multimorbidity (up to 76% at 5 years).
  • Hypertension and lipid disorders act as significant risk factors, elevating incidence rates of individual and multiple disorders.
  • Observed multimorbidity rates differ significantly from expected rates in the presence of these risk factors.

Conclusions:

  • Synergistic relationships exist between health risks and chronic disease progression in multimorbidity.
  • Multilevel temporal Bayesian networks offer a more comprehensive approach to analyzing multimorbidity synergies than traditional methods.