Bayesian Estimation and Model Selection in Group-Based Trajectory Models


Emma Zang and Justin T. Max

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Zang, E., & Max, J. T. (2020). Bayesian Estimation and Model Selection in Group-Based Trajectory Models. Psychological Methods. Advance online publication. DOI: 10.1037/met0000359.
We develop a Bayesian group-based trajectory model (GBTM) and extend it to incorporate dual trajectories and Bayesian model averaging for model selection. Our framework lends itself to many of the standard distributions used in GBTMs, including normal, censored normal, binary, and ordered outcomes. On the model selection front, 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.
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