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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's proficiency in drug...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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)...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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...
Pharmacodynamic Responses: Different Types01:03

Pharmacodynamic Responses: Different Types

Pharmacodynamics is the scientific study of a drug's biochemical or physiological influence on the body. It categorizes responses into continuous, discrete (or categorical), and time-to-event outcomes. Continuous responses yield numerical values within a certain range, such as blood pressure readings and blood glucose levels, gauging the efficacy of antihypertensive and antidiabetic drugs. Discrete responses can be binary, indicating whether a drug has an effect or not, or ordinal, exemplifying...

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Classifying individuals as physiological responders using hierarchical modeling.

Richard J Barker1, Matthew R Schofield

  • 1Department of Mathematics and Statistics, University of Otago, PO Box 56, Dunedin, New Zealand. rbarker@maths.otago.ac.nz

Journal of Applied Physiology (Bethesda, Md. : 1985)
|May 31, 2008
PubMed
Summary
This summary is machine-generated.

Hierarchical modeling accurately classifies subjects as responders or non-responders, even with hidden data. This statistical approach improves inference and explores reasons for varied subject responses.

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Area of Science:

  • Biostatistics
  • Physiology
  • Biomarkers

Background:

  • Accurate subject classification is crucial in biomedical research.
  • Ignoring uncertainty in classification can lead to unreliable statistical inference.
  • Latent (hidden) variables present challenges in subject categorization.

Purpose of the Study:

  • To introduce and illustrate hierarchical modeling for classifying subjects into responder and non-responder groups.
  • To demonstrate how hierarchical models correctly account for latent variables.
  • To enable formal inference addressing the biological basis of response variation.

Main Methods:

  • Utilized hierarchical modeling to infer latent responder status.
  • Incorporated predictor variables and biological relationships to model the hidden variable.
  • Applied the method to a study on hepcidin excretion in female marathon runners.

Main Results:

  • Hierarchical modeling provides a robust framework for handling latent classification variables.
  • The approach facilitates reliable statistical inference by accounting for uncertainty.
  • Demonstrated the utility of the method in a real-world physiological study.

Conclusions:

  • Hierarchical modeling is advantageous for accurate subject classification with latent variables.
  • This method enhances the reliability of statistical inference in biomedical studies.
  • Researchers can use this approach to investigate biological factors influencing subject response.