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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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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...
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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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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.
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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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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.
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Modeling Change in the Presence of Non-Randomly Missing Data: Evaluating A Shared Parameter Mixture Model.

Nisha C Gottfredson1, Daniel J Bauer2, Scott A Baldwin3

  • 1Duke University.

Structural Equation Modeling : a Multidisciplinary Journal
|July 12, 2014
PubMed
Summary

Longitudinal studies can be biased by missing data that depends on individual change trajectories. A new shared-parameter mixture model (SPMM) effectively addresses this non-ignorable missingness, improving trajectory estimates.

Keywords:
Growth Mixture ModelsGrowth ModelsLongitudinal DataMissing DataShared Parameter Mixture Models

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Area of Science:

  • Statistics
  • Longitudinal Data Analysis
  • Biostatistics

Background:

  • Longitudinal research focuses on individual change trajectories over time.
  • Missing data in longitudinal studies can introduce bias if not handled properly.
  • Systematic missingness, termed random coefficient-dependent missingness, is non-ignorable and affects growth models.

Purpose of the Study:

  • To introduce and evaluate a shared-parameter mixture model (SPMM).
  • To test the sensitivity of growth model parameter estimates to random coefficient-dependent missingness.
  • To provide methods for analyzing longitudinal data with non-ignorable missingness.

Main Methods:

  • Development of a shared-parameter mixture model (SPMM).
  • Simulation studies to compare SPMM with standard growth models.
  • Assessment of parameter estimate recovery under various missing data conditions.

Main Results:

  • The SPMM demonstrated robust performance in recovering trajectory estimates.
  • SPMM performed as well as or better than standard growth models across different missing data scenarios.
  • The model effectively accounts for random coefficient-dependent missingness.

Conclusions:

  • The SPMM is a valuable tool for analyzing longitudinal data with non-ignorable missingness.
  • Researchers should consider SPMM to mitigate bias from systematic missing data.
  • Practical guidance is offered for longitudinal data analysts using SPMM.