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Statistics in medicine2010; 29(17); 1839-1856; doi: 10.1002/sim.3956

A two-component nonlinear mixed effects model for longitudinal data, with application to gastric emptying studies.

Abstract: Gastric emptying studies are of great interest in human and veterinary medical research to evaluate effects of medications or diets for promoting gastrointestinal motility and to examine unintended side-effects of new or existing medications, diets, or procedures. Summarizing gastric emptying data is important to allow easier comparison between treatments or groups of subjects and comparisons of results among studies. The standard method for assessing gastric emptying is by using scintigraphy and summarizing the nonlinear emptying of the radioisotope. A popular model for fitting gastric emptying data is the power exponential model. This model can only describes a globally decreasing pattern and thus has the limitation of poorly describing localized intragastric events that can occur during emptying. Hence, we develop a new model for gastric emptying studies to improve population and individual inferences using a mixture of nonlinear mixed effects models. One mixture component is based on a power exponential model which captures globally decreasing patterns. The other is based on a locally extended power exponential model which captures both local bumping and rapid decay. We refer to this mixture model as a two-component nonlinear mixed effects model. The parameters in our model have clear graphical interpretations that provide a more accurate representation and summary of the curves of gastric emptying pattern. Two methods are developed to fit our proposed model: one is the mixture of an Expectation Maximization algorithm and a global two-stage method and the other is the mixture of an Expectation Maximization algorithm and the Monte Carlo Expectation Maximization algorithm. We compare our methods using simulation, showing that the two approaches are comparable to one another. For estimating the variance and covariance matrix, the second approach appears approximately more efficient and is also numerically more stable in some cases. Our new model and approaches are applicable for assessing gastric emptying in human and veterinary medical research and in many other biomedical fields such as pharmacokinetics, toxicokinetics, and physiological research. An example of gastric emptying data from equine medicine is used to demonstrate the advantage of our approaches.
Copyright (c) 2010 John Wiley & Sons, Ltd.
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Publication Date: 2010-07-27 PubMed ID: 20658551PubMed Central: PMC3608145DOI: 10.1002/sim.3956Google Scholar: Lookup
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Summary

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This research paper is about a new statistical model the authors developed to more effectively analyze gastric emptying data, a critical aspect in medical research to evaluate the effects of medication, diets, and gastrointestinal motility.

Importance of Gastric Emptying Data

  • Gastric emptying studies have been identified as a significant research subject in veterinary and human medical research.
  • They are used to evaluate the effects of various factors such as medications, diets, and their side effects, particularly regarding gastrointestinal motility.
  • It is critical to summarize these data effectively to allow easy comparisons among different treatments, subject groups, or even separate studies.

Traditional Methods and Their Limitations

  • The standard method for assessing gastric emptying hinges on the use of scintigraphy and summarizing the nonlinear emptying of the radioisotope. It is popularly known as the power exponential model.
  • However, this model only describes globally decreasing patterns and is therefore not efficient in accurately describing localized intragastric events that can occur during emptying.

Proposed New Model: Two-Component Nonlinear Mixed Effects Model

  • The researchers have developed a new model dubbed the two-component nonlinear mixed effects model, which enhances both population and individual inferences.
  • This model consists of two components; one is based on the power exponential model for globally decreasing patterns, and the other expands on the power exponential model to account for local bumping and rapid decay.
  • The model’s parameters offer clear graphical interpretations for more accurate summaries and representation of gastric emptying patterns.

Methods Used to Fit the Proposed Model

  • The researchers developed two methods to apply their proposed model. One is the Expectation Maximization algorithm combined with a global two-stage method. The other is the Expectation Maximization algorithm used with the Monte Carlo Expectation Maximization algorithm.
  • These two approaches proved to be comparable in simulations set up by the researchers. However, the second approach was found to be more efficient and numerically stable in estimating the variance and covariance matrix.

Applications of the New Model

  • The new model can be applied in human and veterinary medical research for assessing gastric emptying. It also has a potential application in other biomedical fields like pharmacokinetics, toxicokinetics, and physiological research.
  • An example with gastric emptying data from equine medicine was used to demonstrate the effectiveness of the newly proposed approaches.

Cite This Article

APA
Kim I, Cohen ND, Roussel A, Wang N. (2010). A two-component nonlinear mixed effects model for longitudinal data, with application to gastric emptying studies. Stat Med, 29(17), 1839-1856. https://doi.org/10.1002/sim.3956

Publication

Statistics in medicine
ISSN: 1097-0258
NlmUniqueID: 8215016
Country: England
Language: English
Volume: 29
Issue: 17
Pages: 1839-1856

Researcher Affiliations

Kim, Inyoung
  • Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0439, USA. nyoungk@vt.edu
Cohen, Noah D
    Roussel, Allen
      Wang, Naisyin

        MeSH Terms

        • Algorithms
        • Animals
        • Computer Simulation
        • Gastric Emptying / physiology
        • Horses / physiology
        • Humans
        • Longitudinal Studies
        • Models, Biological
        • Nonlinear Dynamics

        Grant Funding

        • R01 CA074552 / NCI NIH HHS
        • R01 CA074552-14 / NCI NIH HHS
        • R29 CA074552 / NCI NIH HHS
        • CA74552 / NCI NIH HHS

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        Citations

        This article has been cited 2 times.
        1. Bertoli D, Steinkohl E, Mark EB, Brock C, Drewes AM, Frøkjaer JB. Quantification of gastric emptying with magnetic resonance imaging in healthy volunteers: A systematic review. Neurogastroenterol Motil 2022 Dec;34(12):e14371.
          doi: 10.1111/nmo.14371pubmed: 35340100google scholar: lookup
        2. Yokrattanasak J, De Gaetano A, Panunzi S, Satiracoo P, Lawton WM, Lenbury Y. A Simple, Realistic Stochastic Model of Gastric Emptying. PLoS One 2016;11(4):e0153297.
          doi: 10.1371/journal.pone.0153297pubmed: 27057750google scholar: lookup
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