Li Hong, Leurgans Sue, Elm Jordan, Gebregziabher Mulugeta
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Department of Neurological Sciences & Preventive Medicine, Rush University Medical Center Chicago, IL, USA.
J Alzheimers Dis. 2019;68(1):173-186. doi: 10.3233/JAD-180580.
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.
阿尔茨海默病（AD）是一种常见且具有毁灭性的疾病，它带来了沉重的经济负担。迫切需要加快努力来识别AD的症状前阶段以及用于对该疾病进行分类的生物标志物。目前，尚无生物标志物能够完美地将个体区分为AD的多个疾病类别（无认知障碍、轻度认知障碍和痴呆）。尽管许多用于诊断的生物标志物及其各种特征正在被研究，但我们缺乏能够充分利用生物标志物准确分类AD的先进统计方法，从而难以单独或联合评估假定的标志物。在本文中，我们提出了两种方法：1）一种向前逐步添加程序，在此程序中，我们将加法逻辑回归模型应用于具有有序多类别的疾病情况。使用这种方法，我们选择并组合多个横断面生物标志物以提高诊断准确性；2）一种通过将奈曼 - 皮尔逊引理扩展到有序的三种疾病类别来构建最佳临界点以区分多个疾病类别的方法。我们通过模拟研究评估所提出模型的稳健性。然后我们将这两种方法应用于宗教团体研究的数据，以检验组合生物标志物的可行性，并比较所提出的方法与现有方法（包括基于模型的方法（有序逻辑回归和二次判别分析）、基于树的方法CART以及尤登指数法）之间的诊断准确性。所提出的两种方法有助于对具有分级而非二元分类的疾病情况进行生物标志物评估。对不同方法性能的评估为如何选择方法来解决研究问题提供了指导。
