Department of Health Sciences, Community and Occupational Medicine Groningen, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Department of Interdisciplinary Social Science, Utrecht University, Utrecht, Netherlands.
BMC Med Res Methodol. 2020 Nov 12;20(1):276. doi: 10.1186/s12874-020-01154-0.
Growth Mixture Modeling (GMM) is commonly used to group individuals on their development over time, but convergence issues and impossible values are common. This can result in unreliable model estimates. Constraining variance parameters across classes or over time can solve these issues, but can also seriously bias estimates if variances differ. We aimed to determine which variance parameters can best be constrained in Growth Mixture Modeling.
To identify the variance constraints that lead to the best performance for different sample sizes, we conducted a simulation study and next verified our results with the TRacking Adolescent Individuals' Lives Survey (TRAILS) cohort.
If variance parameters differed across classes and over time, fitting a model without constraints led to the best results. No constrained model consistently performed well. However, the model that constrained the random effect variance and residual variances across classes consistently performed very poorly. For a small sample size (N = 100) all models showed issues. In TRAILS, the same model showed substantially different results from the other models and performed poorly in terms of model fit.
If possible, a Growth Mixture Model should be fit without any constraints on variance parameters. If not, we recommend to try different variance specifications and to not solely rely on the default model, which constrains random effect variances and residual variances across classes. The variance structure must always be reported Researchers should carefully follow the GRoLTS-Checklist when analyzing and reporting trajectory analyses.
增长混合建模（GMM）常用于根据个体随时间的发展对其进行分组，但收敛问题和不可能的值很常见。这可能导致模型估计不可靠。在不同类别或随时间约束方差参数可以解决这些问题，但如果方差不同，也会严重偏倚估计。我们旨在确定在增长混合建模中可以最好地约束哪些方差参数。
为了确定哪些方差参数可以在不同的样本量下达到最佳性能，我们进行了一项模拟研究，并使用跟踪青少年个体生活调查（TRAILS）队列验证了我们的结果。
如果方差参数在不同类别和随时间变化，拟合无约束模型会导致最佳结果。没有约束的模型表现始终不佳。但是，跨类别的随机效应方差和残差方差的约束模型表现非常差。对于小样本量（N=100），所有模型都显示出问题。在 TRAILS 中，相同的模型与其他模型显示出显著不同的结果，并且在模型拟合方面表现不佳。
如果可能，增长混合模型不应对方差参数施加任何约束。如果不可能，我们建议尝试不同的方差规范，并且不要仅依赖于默认模型，该模型约束了跨类别的随机效应方差和残差方差。方差结构必须始终报告。研究人员在分析和报告轨迹分析时应仔细遵循 GRoLTS 清单。
