Hou Jiayi, Archer Kellie J
Stat Appl Genet Mol Biol. 2015 Feb;14(1):93-111. doi: 10.1515/sagmb-2014-0004.
Abstract An ordinal scale is commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical methodology based on statistical inference, in particular, ordinal modeling has contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) remains smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for more accurate diagnosis and prognosis, high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. To meet the emerging needs, we introduce our proposed model which is a two-stage algorithm: Extend the generalized monotone incremental forward stagewise (GMIFS) method to the cumulative logit ordinal model; and combine the GMIFS procedure with the classical mixed-effects model for classifying disease status in disease progression along with time. We demonstrate the efficiency and accuracy of the proposed models in classification using a time-course microarray dataset collected from the Inflammation and the Host Response to Injury study.
摘要 顺序量表常用于测量医院环境以及转化医学研究中的健康状况和疾病相关结局。此外，重复测量在临床实践中很常见，用于跟踪和监测复杂疾病的进展。基于统计推断的经典方法，特别是顺序建模，有助于分析响应类别有序且协变量数量（p）小于样本量（n）的数据。随着基因组技术越来越多地用于更准确的诊断和预后，产生了协变量数量（p）远大于样本数量（n）的高维数据。为满足新出现的需求，我们介绍了我们提出的模型，它是一种两阶段算法：将广义单调递增前向逐步（GMIFS）方法扩展到累积对数序贯模型；并将GMIFS程序与经典混合效应模型相结合，用于在疾病随时间进展过程中对疾病状态进行分类。我们使用从炎症与宿主对损伤的反应研究中收集的时间进程微阵列数据集，证明了所提出模型在分类中的效率和准确性。
