Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Biostatistics. 2013 Sep;14(4):779-91. doi: 10.1093/biostatistics/kxt017. Epub 2013 May 2.
In studies that compare several diagnostic or treatment groups, subjects may not only be measured on a certain set of feature variables, but also be matched on a number of demographic characteristics and measured on additional covariates. Linear discriminant analysis (LDA) is sometimes used to identify which feature variables best discriminate among groups, while accounting for the dependencies among the feature variables. We present a new approach to LDA for multivariate normal data that accounts for the subject matching used in a particular study design, as well as covariates not used in the matching. Applications are given for post-mortem tissue data with the aim of comparing neurobiological characteristics of subjects with schizophrenia with those of normal controls, and for a post-mortem tissue primate study comparing brain biomarker measurements across three treatment groups. We also investigate the performance of our approach using a simulation study.
在比较多个诊断或治疗组的研究中，研究对象不仅可能在一组特定的特征变量上进行测量，还可能根据一些人口统计学特征进行匹配，并在其他协变量上进行测量。线性判别分析（LDA）有时用于确定哪些特征变量在组间的区分效果最好，同时考虑到特征变量之间的相关性。我们提出了一种新的方法，用于对多元正态数据进行 LDA，该方法考虑了特定研究设计中使用的对象匹配，以及未用于匹配的协变量。应用程序适用于尸检组织数据，目的是比较精神分裂症患者和正常对照者的神经生物学特征，以及比较三种治疗组的脑生物标志物测量的尸检组织灵长类动物研究。我们还使用模拟研究来研究我们方法的性能。
