Latent-variable models for longitudinal data with bivariate ordinal outcomes.
Latent-variable models for longitudinal data with bivariate ordinal outcomes.
复制标题
具有双变量序数结果的纵向数据的潜变量模型。
DOI：
10.1002/sim.2599
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发表时间：
2007
影响因子：
2
通讯作者：
Lesaffre,Emmanuel
中科院分区：
文献类型：
--
作者：
Todem,David;Kim,KyungMann;Lesaffre,Emmanuel
我们使用潜在变量的概念来推导二元序数结果的联合分布，然后扩展模型以允许纵向数据。具体来说，我们使用阈值将观察到的序数结果与二元潜在变量相关联，然后将其建模为线性混合模型。随机效应术语用于将同一受试者的所有重复观察结果联系在一起。两种结果之间的横截面关联是通过双变量潜变量的相关系数建模的，以随机效应为条件。假设给定随机效应的条件独立性，则在随机假设缺失数据的情况下，使用自适应高斯求积进行数值积分来近似边际似然。该模型提供了特定于主题的固定效应参数，但在适当缩放时保留了总体平均解释。这特别适合群体比较和个体水平对比同等重要的情况。来自精神病学试验氟伏沙明（一种抗抑郁药物）研究的数据用于说明该方法。版权所有 © 2006 约翰·威利父子有限公司
We use the concept of latent variables to derive the joint distribution of bivariate ordinal outcomes, and then extend the model to allow for longitudinal data. Specifically, we relate the observed ordinal outcomes using threshold values to a bivariate latent variable, which is then modelled as a linear mixed model. Random effects terms are used to tie all together repeated observations from the same subject. The cross‐sectional association between the two outcomes is modelled through the correlation coefficient of the bivariate latent variable, conditional on random effects. Assuming conditional independence given random effects, the marginal likelihood, under the missing data at random assumption, is approximated using an adaptive Gaussian quadrature for numerical integration. The model provides fixed effects parameters that are subject‐specific, but retain the population‐averaged interpretation when properly scaled. This is particularly well suited for the situation in which population comparisons and individual level contrasts are of equal importance. Data from a psychiatric trial, the Fluvoxamine (an antidepressant drug) study, are used to illustrate the methodology. Copyright © 2006 John Wiley & Sons, Ltd.