Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction
Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction
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DOI：
10.1093/biostatistics/kxh022
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发表时间：
2005-01-01
期刊：
影响因子：
2.1
通讯作者：
Katz, IR
中科院分区：
文献类型：
--
作者：
Elliott, MR;Gallo, JJ;Katz, IR
积极和消极的影响数据经常在精神病护理环境中随着时间的推移而收集，但没有普遍接受的方法可以将这些数据与有用的诊断或治疗联系起来。潜在类分析试图通过基于观察到的数据将受试者分类到K个未观察到的类中的一个来减少数据。潜在的类模型最近被扩展到容纳纵向观察到的数据。我们在贝叶斯框架中扩展了这些方法，以适应连续和离散数据的轨迹。我们考虑是否可以使用潜在类模型来区分患者的基础上观察到的影响分数的轨迹。报告的事件以及是否存在临床抑郁。
Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores. reported events, and presence or absence of clinical depression.