title: Adjusting for matching and covariates in linear discriminant analysis.
abstract: 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.
authors: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
authors: Josephine K
authors: JK
authors: Asafu-Adjei
authors: Allan R
authors: AR
authors: Sampson
authors: Robert A
authors: RA
authors: Sweet
authors: David A
authors: DA
authors: Lewis
mesh_terms: D000818:Animals; D014150:Antipsychotic Agents / Q000494:pharmacology; D015415:Biomarkers; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008251:Macaca; D008297:Male; D012107:Research Design; D012559:Schizophrenia / Q000188:drug therapy / Q000473:pathology
chemical_list: D014150:Antipsychotic Agents; D015415:Biomarkers
keywords: Covariates; Linear discriminant analysis; Matched design; Post-mortem tissue studies; Schizophrenia
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