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Review
. 2020 Oct 30:12:1205-1222.
doi: 10.2147/CLEP.S265287. eCollection 2020.

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

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 Lacasse  1
Affiliations
Review

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

Hermine Lore Nguena Nguefack et al. Clin Epidemiol. .

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.

Keywords: cluster analysis; group-based trajectory modelling; growth mixture modelling; latent class analysis; latent transition analysis; modelling techniques; sequence analysis.

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Conflict of interest statement

The authors declare no conflicts of interest and no financial interests related to this study.

Figures

Figure 1
Figure 1
Pain intensity modelling methods: population average models (top) and trajectory modelling approaches (bottom).
See this image and copyright information in PMC

References

    1. Jung T. An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol Compass. 2008;2(1):302–317.
    1. Ram N, Grimm KJ. Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev. 2009;33(6):565–576. doi: 10.1177/0165025409343765 - DOI - PMC - PubMed
    1. Reinecke J, Seddig D. Growth mixture models in longitudinal research. AStA. 2011;95:415–434. doi: 10.1007/s10182-011-0171-4 - DOI
    1. Barban N, Billar FC. Classifying life course trajectories: a comparison of latent class and sequence analysis. J Royal Statistical Society. 2012;61(5):765–784.
    1. Elmer J, Jones BL, Nagin DS. Using the Beta distribution in group-based trajectory models. BMC Med Res Methodol. 2018;18(1):152. doi: 10.1186/s12874-018-0620-9 - DOI - PMC - PubMed