Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches

Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory...

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Published inClinical epidemiology Vol. 12; pp. 1205 - 1222
Main Authors Nguena Nguefack, Hermine Lore, Pagé, M Gabrielle, Katz, Joel, Choinière, Manon, Vanasse, Alain, Dorais, Marc, Samb, Oumar Mallé, Lacasse, Anaïs
Format Journal Article
LanguageEnglish
Published New Zealand Dove Medical Press Limited 01.01.2020
Taylor & Francis Ltd
Dove
Dove Medical Press
Subjects
Clinical medicine
cluster analysis
Epidemiology
Estimates
group-based trajectory modelling
growth mixture modelling
Health aspects
Latent class analysis
latent transition analysis
modelling techniques
Pain
Population
Random variables
Review
sequence analysis
Terminology
Canada
Quebec
latent transition analysis
group-based trajectory modelling
growth mixture modelling
modelling techniques
sequence analysis
latent class analysis
cluster analysis
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Abstract Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.
AbstractList Hermine Lore Nguena Nguefack,1 M Gabrielle Pagé,2,3 Joel Katz,4 Manon Choinière,2,3 Alain Vanasse,5,6 Marc Dorais,7 Oumar Mallé Samb,1 Anaïs Lacasse1 1Département des Sciences de la santé, Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda, Québec, Canada; 2Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada; 3Département d'anesthésiologie et de médecine de la douleur, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada; 4Department of Psychology, Faculty of Health, York University, Toronto, Ontario, Canada; 5Département de médecine de famille et de médecine d'urgence, Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Sherbrooke, Québec, Canada; 6Centre de recherche du Centre hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Québec, Canada; 7StatSciences Inc., Notre-Dame-de-lL'île-Perrot, Québec, CanadaCorrespondence: Anaïs LacasseDépartement des sciences de la santé, Université du Québec en Abitibi-Témiscamingue (UQAT), 445, Boul. de l'Université, Rouyn-Noranda (Qc) J9X 5E4, Québec, CanadaTel +1 819 762 0971, 2722Email lacassea@uqat.caAbstract: Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.Keywords: modelling techniques, growth mixture modelling, group-based trajectory modelling, latent class analysis, latent transition analysis, cluster analysis, sequence analysis
Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers’ efficiency when it comes to choosing the most appropriate technique that best suits their research questions.
Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions. Keywords: modelling techniques, growth mixture modelling, group-based trajectory modelling, latent class analysis, latent transition analysis, cluster analysis, sequence analysis
Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.
Audience Academic
Author Katz, Joel
Choinière, Manon
Pagé, M Gabrielle
Vanasse, Alain
Samb, Oumar Mallé
Dorais, Marc
Lacasse, Anaïs
Nguena Nguefack, Hermine Lore
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  givenname: Hermine Lore
  surname: Nguena Nguefack
  fullname: Nguena Nguefack, Hermine Lore
– sequence: 2
  givenname: M Gabrielle
  orcidid: 0000-0002-7742-2717
  surname: Pagé
  fullname: Pagé, M Gabrielle
– sequence: 3
  givenname: Joel
  orcidid: 0000-0002-8686-447X
  surname: Katz
  fullname: Katz, Joel
– sequence: 4
  givenname: Manon
  orcidid: 0000-0001-9593-8883
  surname: Choinière
  fullname: Choinière, Manon
– sequence: 5
  givenname: Alain
  surname: Vanasse
  fullname: Vanasse, Alain
– sequence: 6
  givenname: Marc
  surname: Dorais
  fullname: Dorais, Marc
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– sequence: 8
  givenname: Anaïs
  orcidid: 0000-0002-3992-5145
  surname: Lacasse
  fullname: Lacasse, Anaïs
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33154677$$D View this record in MEDLINE/PubMed
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Keywords latent transition analysis
group-based trajectory modelling
growth mixture modelling
modelling techniques
sequence analysis
latent class analysis
cluster analysis
Language English
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2020 Nguena Nguefack et al.
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Snippet Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and...
Hermine Lore Nguena Nguefack,1 M Gabrielle Pagé,2,3 Joel Katz,4 Manon Choinière,2,3 Alain Vanasse,5,6 Marc Dorais,7 Oumar Mallé Samb,1 Anaïs Lacasse1...
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StartPage 1205
SubjectTerms Clinical medicine
cluster analysis
Epidemiology
Estimates
group-based trajectory modelling
growth mixture modelling
Health aspects
Latent class analysis
latent transition analysis
modelling techniques
Pain
Population
Random variables
Review
sequence analysis
Terminology
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Title Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches
URI https://www.ncbi.nlm.nih.gov/pubmed/33154677
https://www.proquest.com/docview/2461111203
https://www.proquest.com/docview/2458037187
https://pubmed.ncbi.nlm.nih.gov/PMC7608582
https://doaj.org/article/cf0b087d0f3f45f8be233bd7653c9620
Volume 12
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