Trajectory analyses in insurance medicine studies: Examples and key methodological aspects and pitfalls
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- dc.contributor.author Serra, Laura
- dc.contributor.author Farrants, Kristin
- dc.contributor.author Alexanderson, Kristina
- dc.contributor.author Ubalde López, Mònica, 1972-
- dc.contributor.author Lallukka, Tea
- dc.date.accessioned 2022-05-31T07:00:45Z
- dc.date.available 2022-05-31T07:00:45Z
- dc.date.issued 2022
- dc.description.abstract Background: Trajectory analyses are being increasingly used in efforts to increase understanding about the heterogeneity in the development of different longitudinal outcomes such as sickness absence, use of medication, income, or other time varying outcomes. However, several methodological and interpretational challenges are related to using trajectory analyses. This methodological study aimed to compare results using two different types of software to identify trajectories and to discuss methodological aspects related to them and the interpretation of the results. Methods: Group-based trajectory models (GBTM) and latent class growth models (LCGM) were fitted, using SAS and Mplus, respectively. The data for the examples were derived from a representative sample of Spanish workers in Catalonia, covered by the social security system (n = 166,192). Repeatedly measured sickness absence spells per trimester (n = 96,453) were from the Catalan Institute of Medical Evaluations. The analyses were stratified by sex and two birth cohorts (1949-1969 and 1970-1990). Results: Neither of the software were superior to the other. Four groups were the optimal number of groups in both software, however, we detected differences in the starting values and shapes of the trajectories between the two software used, which allow for different conclusions when they are applied. We cover questions related to model fit, selecting the optimal number of trajectory groups, investigating covariates, how to interpret the results, and what are the key pitfalls and strengths of using these person-oriented methods. Conclusions: Future studies could address further methodological aspects around these statistical techniques, to facilitate epidemiological and other research dealing with longitudinal study designs.
- dc.format.mimetype application/pdf
- dc.identifier.citation Serra L, Farrants K, Alexanderson K, Ubalde M, Lallukka T. Trajectory analyses in insurance medicine studies: Examples and key methodological aspects and pitfalls. PLoS One. 2022 Feb 11;17(2):e0263810. DOI: 10.1371/journal.pone.0263810
- dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0263810
- dc.identifier.issn 1932-6203
- dc.identifier.uri http://hdl.handle.net/10230/53325
- dc.language.iso eng
- dc.publisher Public Library of Science (PLoS)
- dc.relation.ispartof PLoS One. 2022 Feb 11;17(2):e0263810
- dc.rights © 2022 Serra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Computer software
- dc.subject.keyword Growth factors
- dc.subject.keyword Longitudinal studies
- dc.subject.keyword Entropy
- dc.subject.keyword Graphs
- dc.subject.keyword Polynomials
- dc.subject.keyword Research design
- dc.subject.keyword Social security system
- dc.title Trajectory analyses in insurance medicine studies: Examples and key methodological aspects and pitfalls
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion