Gueorguieva R V, Sanacora G
Division of Biostatistics, Department of Epidemiology and Public Health, School of Medicine, Yale University, 60 College St, New Haven, CT 06520, USA.
Stat Med. 2006 Apr 30;25(8):1307-22. doi: 10.1002/sim.2270.
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.
在生物医学研究中，通常会随时间记录多种疾病严重程度的测量指标。尽管这些指标相互关联，但它们常常被彼此分开分析。与单独分析相比，对结果变量进行联合分析有几个潜在的优势。然而，针对不同类型（离散型和连续型）响应变量的模型在定义和拟合方面具有挑战性。在此，我们提出相关概率单位模型，用于对随时间测量同一潜在疾病严重程度的有序变量和连续变量进行重复测量的联合分析。我们展示了如何重写模型，以便使用标准软件进行最大似然估计和推断。进行模拟研究以评估将响应一起拟合而非分开拟合时的效率提升，并为未来研究指导响应变量的选择。使用来自一项抑郁症临床试验的数据进行说明。
