Longitudinal processes often pose nonlinear change patterns. Latent basis growth models (LBGMs) provide a versatile solution without requiring specific functional forms. Building on the LBGM specification for unequally-spaced waves and individual occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. This provides a unified approach to nonlinear, interconnected trajectories. Simulation studies demonstrate that the proposed model can provide unbiased and accurate estimates with target coverage probabilities for the parameters of interest. Real-world analyses of reading and mathematics scores demonstrates its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.

翻译：纵向过程常呈现非线性变化模式。潜在基增长模型（LBGM）提供了一种无需预设具体函数形式的灵活解决方案。基于Liu和Perera（2023）提出的针对非等距测量波次及个体时点的LBGM设定，我们将LBGM扩展至多变量纵向结果。这为非线性且相互关联的发展轨迹提供了统一分析方法。模拟研究表明，所提模型能够为目标参数提供无偏且精确的估计，并达到预期的覆盖概率。对阅读与数学成绩的实际数据分析验证了该方法在分析时间模式各异的联合发展过程中的有效性。文中附有计算代码。