Extension to the PATHMOX Approach to Detect Which Constructs Differentiate Segments and to Test Factor Invariance: Application to Mental Health Data

  • Tomas Aluja-BanetEmail author
  • Giuseppe Lamberti
  • Antonio Ciampi
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 173)


In this paper we propose an extension to the PATHMOX segmentation algorithm to detect which endogenous latent variables and predictors are responsible for heterogeneity. We also address the problem of factor invariance in the terminal nodes of PATHMOX. We demonstrate the utility of such methodology on real mental health data by investigating the relationship between dementia, depression and delirium.


PATHMOX Latent variables Segmentation 



The data analyzed in this paper were collected with funding from the Canadian Institutes of Health Research (IAO69519), Canadian Institute of Aging & Institute of Gender and Health (CRG-82953) and the Alzheimer Society of Canada and the Canadian Nurses Foundation (07-91). Data were used with permission by J. McCusker and M. G. Cole.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tomas Aluja-Banet
    • 1
    Email author
  • Giuseppe Lamberti
    • 1
  • Antonio Ciampi
    • 2
  1. 1.Universitat Politecnica de Catalunya, Barcelona TechBarcelonaSpain
  2. 2.Department of Epidemiology, Biostatistics, and Occupational HealthMcGill UniversityMontrealCanada

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