What is it about?

We develop a Bayesian group-based trajectory model (GBTM) for the estimation of single and dual trajectories, with normal, censored normal, binary, and ordered outcomes.

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Why is it important?

The main advantage of our method compared to the frequentist GBTM is that we incorporate the Bayesian model averaging technique to substantially simplify and improve the model selection process. GBTMs require the researcher to specify a functional relationship between time and the outcome within each latent group. These relationships are generally polynomials with varying degrees in each group, but can also include additional covariates or other functions of time. When the number of groups is large, the model space can grow prohibitively complex, requiring a time-consuming brute-force search over potentially thousands of models. The approach developed in this article requires just one model fit and has the additional advantage of accounting for uncertainty in model selection. In addition, our Bayesian approach produces more accurate estimates when the sample size is small and makes the calculation of standard errors easier compared to the conventional GBTMs.


I hope our method could help researchers apply group-based trajectory models more easily! The R code and data used in this paper are available in GitHub at https://github.com/jtm508/bayestraj. Vignettes are also provided to help users adapt the code to applications of their own interest.

Emma Zang
Yale University

Read the Original

This page is a summary of: Bayesian estimation and model selection in group-based trajectory models., Psychological Methods, November 2020, American Psychological Association (APA),
DOI: 10.1037/met0000359.
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