Publication

Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data

Sijbrandij, J. J., Hoekstra, T., Almansa, J., Reijneveld, S. A. & Bültmann, U., Dec-2019, In : Advances in Life Course Research. 42, 10 p., 100288.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Sijbrandij, J. J., Hoekstra, T., Almansa, J., Reijneveld, S. A., & Bültmann, U. (2019). Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data. Advances in Life Course Research, 42, [100288]. https://doi.org/10.1016/j.alcr.2019.04.018

Author

Sijbrandij, Jitske J. ; Hoekstra, Tialda ; Almansa, Josué ; Reijneveld, Sijmen A. ; Bültmann, Ute. / Identification of developmental trajectory classes : Comparing three latent class methods using simulated and real data. In: Advances in Life Course Research. 2019 ; Vol. 42.

Harvard

Sijbrandij, JJ, Hoekstra, T, Almansa, J, Reijneveld, SA & Bültmann, U 2019, 'Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data', Advances in Life Course Research, vol. 42, 100288. https://doi.org/10.1016/j.alcr.2019.04.018

Standard

Identification of developmental trajectory classes : Comparing three latent class methods using simulated and real data. / Sijbrandij, Jitske J.; Hoekstra, Tialda; Almansa, Josué; Reijneveld, Sijmen A.; Bültmann, Ute.

In: Advances in Life Course Research, Vol. 42, 100288, 12.2019.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Sijbrandij JJ, Hoekstra T, Almansa J, Reijneveld SA, Bültmann U. Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data. Advances in Life Course Research. 2019 Dec;42. 100288. https://doi.org/10.1016/j.alcr.2019.04.018


BibTeX

@article{d299f6bb16e54d57b8c42147ec4caa0d,
title = "Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data",
abstract = "Introduction Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes. Methods We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals’ Lives Survey (TRAILS, N = 2227). Results Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance. Conclusions Growth mixture modeling seems most suitable to identify developmental trajectory classes.",
keywords = "Developmental trajectory, Growth Mixture Modeling, Latent class analysis, Latent class growth analysis, Longitudinal data analysis, Monte Carlo simulation study",
author = "Sijbrandij, {Jitske J.} and Tialda Hoekstra and Josu{\'e} Almansa and Reijneveld, {Sijmen A.} and Ute B{\"u}ltmann",
year = "2019",
month = "12",
doi = "10.1016/j.alcr.2019.04.018",
language = "English",
volume = "42",
journal = "Advances in Life Course Research",
issn = "1040-2608",
publisher = "ELSEVIER SCI LTD",

}

RIS

TY - JOUR

T1 - Identification of developmental trajectory classes

T2 - Comparing three latent class methods using simulated and real data

AU - Sijbrandij, Jitske J.

AU - Hoekstra, Tialda

AU - Almansa, Josué

AU - Reijneveld, Sijmen A.

AU - Bültmann, Ute

PY - 2019/12

Y1 - 2019/12

N2 - Introduction Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes. Methods We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals’ Lives Survey (TRAILS, N = 2227). Results Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance. Conclusions Growth mixture modeling seems most suitable to identify developmental trajectory classes.

AB - Introduction Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes. Methods We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals’ Lives Survey (TRAILS, N = 2227). Results Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance. Conclusions Growth mixture modeling seems most suitable to identify developmental trajectory classes.

KW - Developmental trajectory

KW - Growth Mixture Modeling

KW - Latent class analysis

KW - Latent class growth analysis

KW - Longitudinal data analysis

KW - Monte Carlo simulation study

U2 - 10.1016/j.alcr.2019.04.018

DO - 10.1016/j.alcr.2019.04.018

M3 - Article

VL - 42

JO - Advances in Life Course Research

JF - Advances in Life Course Research

SN - 1040-2608

M1 - 100288

ER -

ID: 96371923