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Related Concept Videos

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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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.
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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.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Hierarchical shrinkage priors and model fitting for high-dimensional generalized linear models.

Nengjun Yi1, Shuangge Ma

  • 1University of Alabama, Birmingham, AL, USA.

Statistical Applications in Genetics and Molecular Biology
|November 30, 2012
PubMed
Summary

This study introduces hierarchical priors for generalized linear models, improving variable selection and coefficient estimation in genetic studies. These methods effectively handle correlated predictors by leveraging group information for enhanced statistical analysis.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-dimensional genetic data often contain highly correlated predictors within biological groups.
  • Simultaneous variable selection and coefficient estimation are crucial for identifying relevant genetic factors.
  • Existing methods may not fully leverage the hierarchical structure of predictor variables.

Purpose of the Study:

  • To develop novel hierarchical prior distributions for generalized linear models.
  • To incorporate the hierarchical structure of predictor variables for improved statistical modeling.
  • To enable simultaneous variable selection and coefficient estimation in high-dimensional genetic data.

Main Methods:

  • Proposed two hierarchical prior distributions: hierarchical Cauchy and double-exponential.
  • Incorporated variable-specific and group-specific tuning parameters for flexible shrinkage.
  • Utilized expectation-maximization (EM) algorithms within iteratively weighted least squares in R.
  • Developed the BhGLM R package for practical implementation.

Main Results:

  • Demonstrated effective handling of correlated predictors by pooling information within groups.
  • Showcased the utility of hierarchical priors in identifying genetic polymorphisms for mouse survival.
  • Simulation studies confirmed the performance of the proposed methods.

Conclusions:

  • Hierarchical priors offer a robust approach for variable selection and coefficient estimation in high-dimensional genetic studies.
  • The proposed methods enhance statistical power by leveraging predictor variable groupings.
  • The BhGLM package provides a valuable tool for researchers in statistical genetics.