Bartolucci Al, Bae Sejong, Singh Karan, Griffith H Randall
Department of Biostatistics, Alzheimer's Disease Research Center, University of Alabama at Birmingham, USA.
Math Comput Simul. 2009 Nov;80(3):561-571. doi: 10.1016/j.matcom.2009.09.002.
The mini mental state examination (MMSE) is a common tool for measuring cognitive decline in Alzhiemer's Disease (AD) subjects. Subjects are usually observed for a specified period of time or until death to determine the trajectory of the decline which for the most part appears to be linear. However, it may be noted that the decline may not be modeled by a single linear model over a specified period of time. There may be a point called a change point where the rate or gradient of the decline may change depending on the length of time of observation. A Bayesian approach is used to model the trajectory and determine an appropriate posterior estimate of the change point as well as the predicted model of decline before and after the change point. Estimates of the appropriate parameters as well as their posterior credible regions or regions of interest are established. Coherent prior to posterior analysis using mainly non informative priors for the parameters of interest is provided. This approach is applied to an existing AD database.
简易精神状态检查表（MMSE）是测量阿尔茨海默病（AD）患者认知衰退的常用工具。通常会对患者进行一段特定时间的观察，或直至其死亡，以确定衰退轨迹，在大多数情况下，这种衰退似乎呈线性。然而，需要注意的是，在特定时间段内，衰退情况可能无法用单一的线性模型来模拟。可能存在一个称为变化点的点，根据观察时间的长短，衰退的速率或斜率可能会发生变化。采用贝叶斯方法对轨迹进行建模，并确定变化点的合适后验估计值，以及变化点前后衰退的预测模型。建立了合适参数的估计值及其后验可信区间或感兴趣区域。主要针对感兴趣的参数使用非信息先验，提供了连贯的先验到后验分析。该方法应用于一个现有的AD数据库。
