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Related Concept Videos

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.
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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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...
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)...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Objective Bayes model selection in probit models.

Luis Leon-Novelo1, Elías Moreno, George Casella

  • 1Department of Statistics, University of Florida, Gainesville, FL 32611, USA.

Statistics in Medicine
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel variable selection method for probit regression models using objective priors and posterior probabilities. A stochastic search algorithm efficiently handles numerous covariates, aiding in pneumonia prediction from gene expression data.

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Area of Science:

  • Biostatistics
  • Statistical modeling
  • Machine learning in bioinformatics

Background:

  • Variable selection is crucial for building interpretable and accurate statistical models.
  • Probit regression models are widely used for binary categorical outcomes.
  • Existing variable selection methods may struggle with a large number of covariates or require tuning parameters.

Purpose of the Study:

  • To develop a new, objective variable selection procedure for probit regression models.
  • To introduce a stochastic search algorithm for efficiently exploring vast model spaces.
  • To provide a practical tool for analyzing high-dimensional biological data, such as gene expression.

Main Methods:

  • Utilizes objective intrinsic priors for model parameters, avoiding tuning.
  • Ranks candidate models based on their posterior probabilities.
  • Implements a stochastic search algorithm to navigate large sets of potential models.
  • Allows control over model dimension, accommodating more covariates than observations.

Main Results:

  • The proposed procedure effectively ranks models based on posterior probabilities.
  • The stochastic search algorithm efficiently explores the model space, even with many covariates.
  • Simulations demonstrate the procedure's robust performance.
  • Successful application to a gene expression dataset for pneumonia prediction.

Conclusions:

  • The new variable selection procedure offers an objective and efficient approach for probit regression.
  • The stochastic search algorithm is particularly valuable for high-dimensional datasets.
  • The method and associated R package (varselectIP) provide a useful tool for biological data analysis.