Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.
Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.
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DOI：
10.1037/met0000048
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发表时间：
2015-12
影响因子：
7
通讯作者：
Shiyko MP
中科院分区：
文献类型：
--
作者：
Dziak JJ;Li R;Tan X;Shiffman S;Shiyko MP
行为科学家越来越多地收集密集的纵向数据(ILD)，在这些数据中，现象被高频地实时测量。在许多这样的研究中，描述重要变量随时间的变化模式以及变量之间关系的变化性质是很有意义的。个体对感兴趣变量的轨迹可能远不是线性的，感兴趣变量和相关协变量之间的预测关系也可能随着时间的推移而以非线性的方式变化。时变效应模型(TVEM；参见)通过允许回归系数是时间的平滑、非线性函数而不是常量来满足这些需求。然而，不仅观察到的协变量，而且未知的潜在变量也可能与结果相关。也就是说，回归系数可能会随着时间的推移而变化，也会随着不同类型的个体而变化。因此，我们描述了一种有限混合版本的TVEM，在这种情况下，种群是异质的，其中单个轨迹将掩盖重要的个体间差异。这种扩展的方法，MixTVEM，结合了有限混合建模和非参数或半参数回归建模，以描述不同潜在的个人类别随时间变化的复杂模式。通过一个戒烟研究的实证例子，证明了该方法的有效性。我们提供了一个多功能的SAS宏和R函数来安装MixTVEM。
Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, inter-individual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semi-parametric regression modeling, in order to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs.