Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA.
Center on Aging and Human Development, Duke University Medical Center, Durham, North Carolina, USA.
Stat Med. 2023 Jun 30;42(14):2420-2438. doi: 10.1002/sim.9730. Epub 2023 Apr 5.
Modeling longitudinal trajectories and identifying latent classes of trajectories is of great interest in biomedical research, and software to identify latent classes of such is readily available for latent class trajectory analysis (LCTA), growth mixture modeling (GMM) and covariance pattern mixture models (CPMM). In biomedical applications, the level of within-person correlation is often non-negligible, which can impact the model choice and interpretation. LCTA does not incorporate this correlation. GMM does so through random effects, while CPMM specifies a model for within-class marginal covariance matrix. Previous work has investigated the impact of constraining covariance structures, both within and across classes, in GMMs-an approach often used to solve convergence problems. Using simulation, we focused specifically on how misspecification of the temporal correlation structure and strength, but correct variances, impacts class enumeration and parameter estimation under LCTA and CPMM. We found (1) even in the presence of weak correlation, LCTA often does not reproduce original classes, (2) CPMM performs well in class enumeration when the correct correlation structure is selected, and (3) regardless of misspecification of the correlation structure, both LCTA and CPMM give unbiased estimates of the class trajectory parameters when the within-individual correlation is weak and the number of classes is correctly specified. However, the bias increases markedly when the correlation is moderate for LCTA and when the incorrect correlation structure is used for CPMM. This work highlights the importance of correlation alone in obtaining appropriate model interpretations and provides insight into model choice.
建模纵向轨迹并识别轨迹的潜在类别在生物医学研究中非常重要，并且有软件可用于识别此类潜在类别的轨迹，包括潜在类别轨迹分析（LCTA）、增长混合建模（GMM）和协方差模式混合模型（CPMM）。在生物医学应用中，个体内的相关性水平通常不可忽略，这会影响模型选择和解释。LCTA 不包含这种相关性。GMM 通过随机效应来实现，而 CPMM 则为类内边际协方差矩阵指定模型。先前的工作已经研究了在 GMM 中约束协方差结构（包括类内和类间）的影响，这是一种常用于解决收敛问题的方法。我们使用模拟专门研究了在 LCTA 和 CPMM 下，时间相关结构和强度的错误指定，但正确的方差如何影响类别枚举和参数估计。我们发现：（1）即使存在弱相关，LCTA 也常常无法再现原始类别；（2）当选择正确的相关结构时，CPMM 在类别枚举方面表现良好；（3）无论相关结构的指定是否有误，当个体内相关性较弱且正确指定类别数量时，LCTA 和 CPMM 都会对类别轨迹参数给出无偏估计。但是，当相关性为中等时，LCTA 的偏差会明显增加，而 CPMM 则使用不正确的相关结构。这项工作强调了相关性在获得适当的模型解释方面的重要性，并为模型选择提供了深入的见解。
