Abstract

In studies related to social or medical sciences, ordinal responses are often recorded repeatedly over time on a subject. A semi-parametric model with spline smoothing has been considered to capture the temporal trend exhibited in the longitudinal data. In addition, information on covariates and/or responses may not be available in one or more visit. A dynamic model for both missing responses and covariates is considered here. The parameters are estimated by adopting MCNREM methodology. A detailed simulation study has been performed to justify the utility of the proposed model. The model is applied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data.

摘要

在与社会或医学科学相关的研究中，通常会随着时间的推移反复记录一个主题的序数反应。已经考虑使用样条平滑的半参数模型来捕捉纵向数据中表现出的时间趋势。此外，关于协变量和/或响应的信息可能无法在一次或多次访问中获得。这里考虑了缺失响应和协变量的动态模型。采用 MCNREM 方法估计参数。已经进行了详细的模拟研究以证明所提出模型的实用性。该模型应用于阿尔茨海默病神经影像学倡议 (ADNI) 数据。