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Related Concept Videos

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
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 Algorithms for Numerical Problem Solving01:29

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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...
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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)...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...

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Continuous time simulation and discretized models for cost-effectiveness analysis.

Marta O Soares1, Luísa Canto E Castro

  • 1Centre for Health Economics, University of York, UK. marta.soares@york.ac.uk

Pharmacoeconomics
|November 3, 2012
PubMed
Summary
This summary is machine-generated.

Decision-analytic models require careful design for cost-effectiveness analysis. Discretized models with continuity corrections offer accurate results, preferable to continuous-time simulation models when possible.

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Area of Science:

  • Health economics
  • Decision analysis
  • Mathematical modeling

Background:

  • Cost-effectiveness analysis (CEA) relies on decision-analytic models.
  • Accurate modeling of time is crucial, often requiring continuous-time representations.
  • Models evaluated in continuous time may lack closed-form solutions, necessitating approximations.

Purpose of the Study:

  • To address the design of decision-analytic models for CEA.
  • To compare continuous-time simulation models and discretized models.
  • To explore the impact of approximations on survival, cost-effectiveness, and incremental comparisons.

Main Methods:

  • Development of stylized examples for implementing continuous-time simulation and discretized models.
  • Investigation of factors influencing approximation choice: cycle length, precision, continuity corrections, and rate-to-probability conversion.
  • Evaluation of the impact of discretization methods on model outputs.

Main Results:

  • Discretized models approximate continuous-time results better with shorter cycle lengths.
  • Continuity corrections in discretized models permit longer cycle lengths without significant bias.
  • Appropriate discretization with continuity correction yields unbiased results at higher cycle lengths.
  • Continuous-time simulation models are stochastic; precision depends on sample size.

Conclusions:

  • Acknowledging and managing bias in cost-effectiveness estimation is vital.
  • Cohort discretized models are generally preferable to continuous-time simulation models when applicable.
  • When conventional discretized models are insufficient, alternative designs like simulation models are necessary.