Statistical Methodology for Multiclass Classifications: Applications to Dementia

Abstract

Alzheimer’s disease (AD) is a common, devastating disease which carries a heavy economic burden. Accelerated efforts to identify presymptomatic stages of AD and biomarkers to classify the disease are urgent needs. Currently, no biomarkers can perfectly discriminate individuals into multiple disease categories of AD (no cognitive impairment, mild cognitive impairment, and dementia). Although many biomarkers for diagnosis and their various features are being studied, we lack advanced statistical methods which can fully utilize biomarkers to classify AD accurately, thereby facilitating evaluation of putative markers both alone and in combination. In this paper, we propose two approaches: 1) a forward addition procedure in which we adapt an additive logistic regression model to the setting for disease with ordered multiple categories. Using this approach, we select and combine multiple cross-sectional biomarkers to improve diagnostic accuracy, and 2) a method by extending the Neyman-Pearson Lemma to the ordered three disease categories to construct optimal cutoff points to distinguish multiple disease categories. We evaluate the robustness of the proposed model using a simulation study. Then we apply these two methods to data from the Religious Orders Study to examine the feasibility of combining biomarkers, and compare the diagnostic accuracy between the proposed methods and existing methods including model-based methods (ordinal logistic regression and quadratic discriminant analysis), a tree-based method CART, and the Youden index method. The two proposed methods facilitate evaluations of biomarkers for conditions with graded, rather than binary, classifications. The evaluation of the performance of different approaches provides guidance of how to choose approaches to address research questions.