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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...
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...
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...
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)...
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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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...

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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

A penalized likelihood approach for mixture cure models.

Fabien Corbière1, Daniel Commenges, Jeremy M G Taylor

  • 1INSERM U897 Biostatistique, Bordeaux F-33076, France. f.corbiere@envt.fr

Statistics in Medicine
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new penalized likelihood method for analyzing survival data with a cured fraction. The approach offers flexible modeling of the hazard function and accurate variance estimation for cure models.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Standard survival models are inadequate for data with a cured fraction.
  • Mixture cure models address this by considering susceptible and non-susceptible individuals.
  • Accurate estimation of survival functions and parameter variances is crucial in cure models.

Purpose of the Study:

  • To propose a flexible penalized likelihood approach for mixture cure models.
  • To enable flexible modeling of the hazard function for susceptible individuals using M-splines.
  • To facilitate direct computation of regression parameter variances via the Hessian matrix.

Main Methods:

  • Utilized a penalized likelihood framework.
  • Employed M-splines for flexible hazard function modeling in susceptible populations.
  • Applied the inverse of the Hessian matrix for direct variance estimation.

Main Results:

  • The proposed penalized likelihood method allows for flexible hazard modeling.
  • Direct computation of parameter variances is feasible using the Hessian matrix.
  • The method was illustrated using a cancer study dataset.

Conclusions:

  • The penalized likelihood approach with M-splines provides a flexible and effective method for mixture cure models.
  • This method improves the estimation of survival functions and parameter variances.
  • The approach is applicable to real-world data, such as in cancer studies.