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. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译：

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基于组的轨迹模型中的贝叶斯估计和模型选择。

我们开发了一个基于贝叶斯组的轨迹模型 (GBTM)，并将其扩展为结合双轨迹和贝叶斯模型平均用于模型选择。我们的框架适用于 GBTM 中使用的许多标准分布，包括正态、删失正态、二元和有序结果。在模型选择方面，GBTM 要求研究人员在每个潜在组内指定时间和结果之间的函数关系。这些关系通常是在每组中具有不同程度的多项式，但也可以包括额外的协变量或其他时间函数。当组的数量很大时，模型空间会变得异常复杂，需要对可能数千个模型进行耗时的蛮力搜索。本文开发的方法只需要一个模型拟合，并且具有考虑模型选择不确定性的额外优势。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）