View in EDS

A latent class location-scale regression model with an application to calorie intake data.

Saved in:
Bibliographic Details
Title: A latent class location-scale regression model with an application to calorie intake data.
Authors: Zhang, Xingruo (AUTHOR), Siddique, Juned (AUTHOR), Spring, Bonnie (AUTHOR), Hedeker, Donald (AUTHOR)
Source: Journal of Behavioral Medicine. Apr2026, Vol. 49 Issue 2, p275-285. 11p.
Subjects: Statistical models, Computer simulation, Weight loss, Food consumption, Research funding, Computer software, Probability theory, Regulation of body weight, Structural equation modeling, Longitudinal method, Food habits, Regression analysis
Abstract: This study introduces an innovative approach for analyzing longitudinal behavioral data with hidden patterns in mean (location) and intraindividual variability (scale) trajectories, using location-scale regressions with latent classes in both the location and scale parts of the model. A full Bayesian approach using Stan is adopted for the estimation of the model parameters. Using simulation studies, we demonstrate that our latent class model yields more precise and informative results, especially regarding the scale, in data exhibiting hidden patterns. Simulation results also show that our model can achieve unbiased parameter estimates as well as a high correct classification rate without over-identifying latent classes in data lacking hidden heterogeneity. Our study equips researchers with a practical tool for subgrouping subjects based on both mean and within-subject variability trajectories of longitudinal outcomes. As an illustration, the latent class model is applied to calorie intake data from a weight loss management study. The integration of latent classes into intraindividual variability trajectories of calorie intake facilitates an understanding of dietary behavior consistency, aiding in personalized weight management interventions. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Behavioral Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 194452498
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A latent class location-scale regression model with an application to calorie intake data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xingruo%22">Zhang, Xingruo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Siddique%2C+Juned%22">Siddique, Juned</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spring%2C+Bonnie%22">Spring, Bonnie</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hedeker%2C+Donald%22">Hedeker, Donald</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Behavioral+Medicine%22">Journal of Behavioral Medicine</searchLink>. Apr2026, Vol. 49 Issue 2, p275-285. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Weight+loss%22">Weight loss</searchLink><br /><searchLink fieldCode="DE" term="%22Food+consumption%22">Food consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Regulation+of+body+weight%22">Regulation of body weight</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Food+habits%22">Food habits</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study introduces an innovative approach for analyzing longitudinal behavioral data with hidden patterns in mean (location) and intraindividual variability (scale) trajectories, using location-scale regressions with latent classes in both the location and scale parts of the model. A full Bayesian approach using Stan is adopted for the estimation of the model parameters. Using simulation studies, we demonstrate that our latent class model yields more precise and informative results, especially regarding the scale, in data exhibiting hidden patterns. Simulation results also show that our model can achieve unbiased parameter estimates as well as a high correct classification rate without over-identifying latent classes in data lacking hidden heterogeneity. Our study equips researchers with a practical tool for subgrouping subjects based on both mean and within-subject variability trajectories of longitudinal outcomes. As an illustration, the latent class model is applied to calorie intake data from a weight loss management study. The integration of latent classes into intraindividual variability trajectories of calorie intake facilitates an understanding of dietary behavior consistency, aiding in personalized weight management interventions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Behavioral Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=194452498
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10865-025-00613-7
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 275
    Subjects:
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Weight loss
        Type: general
      – SubjectFull: Food consumption
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Computer software
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Regulation of body weight
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Food habits
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: A latent class location-scale regression model with an application to calorie intake data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Xingruo
      – PersonEntity:
          Name:
            NameFull: Siddique, Juned
      – PersonEntity:
          Name:
            NameFull: Spring, Bonnie
      – PersonEntity:
          Name:
            NameFull: Hedeker, Donald
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 01607715
          Numbering:
            – Type: volume
              Value: 49
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Behavioral Medicine
              Type: main
ResultId 1