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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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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.
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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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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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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.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Mechanistic Models: Overview of Compartment Models01:21

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281
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...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Reference-based pattern-mixture models for analysis of longitudinal binary data.

Kaifeng Lu1

  • 1Statistical Science, Allergan plc, Madison, NJ, USA.

Statistical Methods in Medical Research
|July 24, 2020
PubMed
Summary

Pattern-mixture models (PMMs) offer sensitivity analyses for longitudinal binary data. This study extends PMMs for binary outcomes, providing robust methods for estimating treatment effectiveness in clinical trials.

Keywords:
Estimandmissing datamultiple imputationmultivariate probit modelpattern-mixture modelsensitivity analysis

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trials

Background:

  • Pattern-mixture models (PMMs) are used for sensitivity analyses in clinical trials, particularly for continuous outcomes.
  • Methodology for PMMs with longitudinal binary data is less established compared to continuous data.
  • Accurate estimation of treatment effectiveness requires robust methods for handling missing data.

Purpose of the Study:

  • To formulate copy-reference and jump-to-reference PMMs for longitudinal binary data.
  • To investigate maximum likelihood, Bayesian, and multiple imputation methods for PMM estimation.
  • To evaluate the performance of these methods through simulations and a real-world bipolar mania study.

Main Methods:

  • Utilized a multivariate probit model with latent variables to define PMMs for binary longitudinal data.
  • Employed maximum likelihood, Bayesian, and multiple imputation techniques for parameter estimation.
  • Conducted simulation studies to assess method performance under various scenarios.

Main Results:

  • The study successfully formulated PMMs for longitudinal binary data.
  • Evaluated the performance of different estimation methods (ML, Bayesian, MI) through simulations.
  • Demonstrated the application of these methods using data from a bipolar mania clinical trial.

Conclusions:

  • The proposed PMM framework provides a valuable tool for analyzing longitudinal binary data with missing outcomes.
  • The study offers practical guidance on selecting appropriate estimation methods for treatment effectiveness.
  • These methods enhance the reliability of inferences in clinical trials involving binary endpoints.