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Related Concept Videos

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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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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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,...
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Longitudinal Studies01:26

Longitudinal Studies

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

288
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...
288
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Bayesian latent time joint mixed effect models for multicohort longitudinal data.

Dan Li1, Samuel Iddi1,2, Wesley K Thompson3

  • 11 Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA.

Statistical Methods in Medical Research
|November 24, 2017
PubMed
Summary
This summary is machine-generated.

Understanding long-term neurodegenerative disease progression, like Alzheimer's disease, is crucial for effective intervention. This study introduces a novel statistical model to analyze short-term data for long-term disease dynamics insights.

Keywords:
Hierarchical Bayesian modelsjoint mixed effects modelslatent time shiftmulticohort longitudinal data

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Computational Biology

Background:

  • Characterizing long-term disease progression in neurodegenerative conditions like Parkinson's and Alzheimer's is essential for developing effective interventions.
  • Natural history studies often collect longitudinal data from multiple disease stages but over limited timeframes.

Purpose of the Study:

  • To develop a statistical model for characterizing long-term disease dynamics using short-term longitudinal data.
  • To provide a framework for understanding disease trajectories from onset to end-stage in neurodegenerative diseases.

Main Methods:

  • A latent time joint mixed effects model was proposed to analyze disease progression.
  • Markov chain Monte Carlo (MCMC) methods were employed for model estimation, selection, and inference.
  • The model was validated through simulation studies and applied to real-world data.

Main Results:

  • The proposed latent time joint mixed effects model successfully characterized long-term disease dynamics.
  • The methodology demonstrated robust estimation and inference capabilities using short-term data.
  • Application to Alzheimer's Disease Neuroimaging Initiative data showcased the model's practical utility.

Conclusions:

  • The developed statistical model offers a powerful approach to inferring long-term disease trajectories from limited longitudinal data.
  • This methodology can enhance our understanding of neurodegenerative disease progression, aiding in the development of targeted interventions.
  • The findings have implications for the design and analysis of future clinical studies in neurodegenerative diseases.