Joint analysis of repeatedly observed continuous and ordinal measures of disease severity
Joint analysis of repeatedly observed continuous and ordinal measures of disease severity
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DOI：
10.1002/sim.2270
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发表时间：
2006-04-30
影响因子：
2
通讯作者：
Sanacora, G
中科院分区：
文献类型：
--
作者：
Gueorguieval, RV;Sanacora, G
在生物医学研究中，经常记录多种疾病严重程度随时间的变化。尽管这些措施相互关联，但它们往往是分开分析的。结果变量的联合分析比单独分析有几个潜在的优势。然而，不同类型（离散和连续）的响应变量的模型很难定义和拟合。在此，我们提出了相关概率模型，用于对随时间测量相同潜在疾病严重程度的有序和连续变量的重复测量进行联合分析。我们演示了如何重写模型，以便可以用标准软件执行最大似然估计和推理。进行模拟研究是为了评估将响应组合在一起而不是单独拟合的效率增益，并指导未来研究的响应变量选择。本文用一项抑郁症临床试验的数据作说明。版权所有(c) 2005 John Wiley & Sons, Ltd。
In biomedical studies often multiple measures of disease severity are recorded over time. Although correlated, such measures are frequently analysed separately of one another. Joint analysis of the outcomes variables has several potential advantages over separate analyses. However, models for response variables of different types (discrete and continuous) are challenging to define and to fit. Herein we propose correlated probit models for joint analysis of repeated measurements on ordinal and continuous variables measuring the same underlying disease severity over time. We demonstrate how to rewrite the models so that maximum-likelihood estimation and inference can be performed with standard software. Simulation studies are performed to assess efficiency gains in fitting the responses together rather than separately and to guide response variable selection for future studies. Data from a depression clinical trial are used for illustration. Copyright (c) 2005 John Wiley & Sons, Ltd.