Open Access<p>Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches</p>
Author(s) -
Hermine Lore Ngueguefack,
M. Gabrielle Pagé,
Joel Katz,
Ma Choinière,
Alain Vanasse,
Marc Dorais,
Oumar Mallé Samb,
Anaïs Lacasse
Publication year - 2020
Publication title -
clinical epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.868
H-Index - 58
ISSN - 1179-1349
DOI - 10.2147/clep.s265287
Subject(s) - trajectory , latent class model , terminology , narrative review , class (philosophy) , consistency (knowledge bases) , computer science , latent variable , population , data science , econometrics , data mining , medicine , machine learning , artificial intelligence , mathematics , linguistics , philosophy , physics , environmental health , astronomy , intensive care medicine
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.