论文标题
分解对时变协变量的纵向结果的影响分解为基线效应和时间效应
Decomposing Impact on Longitudinal Outcome of Time-varying Covariate into Baseline Effect and Temporal Effect
论文作者
论文摘要
随着时间的流逝，纵向过程通常相互关联。因此，重要的是研究发展过程之间的关联并了解其共同发展。具有时变协变量（TVC）的潜在生长曲线模型（LGCM）提供了一种估计TVC对纵向结果的影响的方法，同时同时建模结果的变化。但是，它不允许TVC预测随机生长系数的变化。我们建议使用三种方法来解决此限制，将TVC的效果分解为初始性状和颞态。在每种方法中，TVC的基线被视为初始特征，并且通过对基线值的随机截距和斜率进行回归而获得相应的效果。颞状态的表征为（1）间隔特定的斜率，（2）间隔特定的变化，或（3）在每个测量场合（根据方法）的基线变化。我们通过模拟和现实世界数据分析来证明我们的方法，假设纵向结果是线性线性功能形式。结果表明，具有分解的TVC的LGCM可以通过目标置信区间提供无偏和精确的估计值。我们还为这些方法提供了使用常用的线性和非线性函数的OpenMX和MPLUS 8代码。
Longitudinal processes are often associated with each other over time; therefore, it is important to investigate the associations among developmental processes and understand their joint development. The latent growth curve model (LGCM) with a time-varying covariate (TVC) provides a method to estimate the TVC's effect on a longitudinal outcome while simultaneously modeling the outcome's change. However, it does not allow the TVC to predict variations in the random growth coefficients. We propose decomposing the TVC's effect into initial trait and temporal states using three methods to address this limitation. In each method, the baseline of the TVC is viewed as an initial trait, and the corresponding effects are obtained by regressing random intercepts and slopes on the baseline value. Temporal states are characterized as (1) interval-specific slopes, (2) interval-specific changes, or (3) changes from the baseline at each measurement occasion, depending on the method. We demonstrate our methods through simulations and real-world data analyses, assuming a linear-linear functional form for the longitudinal outcome. The results demonstrate that LGCMs with a decomposed TVC can provide unbiased and precise estimates with target confidence intervals. We also provide OpenMx and Mplus 8 code for these methods with commonly used linear and nonlinear functions.
