Detecting early disease using variability in markers under informative censoring

描述(申请人提供)：发现早期疾病，例如阿尔茨海默病(AD)、所有导致痴呆、虚弱、住院等，是发展的关键一步 预防或延缓疾病的干预措施，是许多老龄化研究的重要目标。对于纵向研究，时间依赖的受试者操作特征(ROC)分析整合了ROC分析中的时间维度，是评估不同时间段随访期间预测疾病发生的标志物的预测能力的有用而有力的方法。然而，在最常用的模型中，一个内在的假设是，审查过程是随机的，这一假设因死亡而被违反，因为死亡与疾病有关。由于辍学而进行的审查通常也是非随机的，因为它被认为与健康状况不佳和负面结果有关，比如老龄化研究中认知能力的加速下降。忽视对疾病的信息性审查可能会导致错误和误导性的结果。此外，有大量证据表明，除了 这些标志物的平均值与疾病有关。因此，利用异质性方差来提高诊断准确率是很重要的。然而，大多数ROC分析忽略了它，这可能导致不准确或误导性的诊断。在时间相关的ROC分析中，缺乏处理信息性审查和利用标记物的异质性差异的统计方法。我们提出了一种时间相关的ROC方法，该方法利用标记物的异质性，并考虑到非随机审查来识别早期疾病。与衰老相关的基因多态(例如，ApoE4、CETPV405V)以及社会和行为协变量(即，感知的压力、个性、抑郁和焦虑)的影响也将被检验。我们预计，我们建议的方法将应用于EAS的数据，通过提供更准确的疾病发病识别，从而指导未来预防与衰老相关的疾病的努力，以便更有效地应用预防或治疗程序。此外，这些方法将具有广泛的适用性，因为所有的纵向老龄化研究都有类似的异质性方差和信息量审查问题。

DESCRIPTION (provided by applicant): Detecting early disease, e.g., Alzheimer's disease (AD), all cause dementia, frailty, institutionalization, etc., is a crucial step towards developing interventions for preventing or delaying disease, and is an important goal in many aging studies. For longitudinal studies, time- dependent receiver operating characteristics (ROC) analysis, which integrates the time dimension in ROC analysis, is a useful and powerful approach to evaluate the prognostic capacity of markers for predicting incident disease during various length of follow-up. However, an intrinsic assumption in most commonly used models is that the censoring process is random , an assumption violated for censoring due to death, since death is associated with disease. Censoring due to drop out is also often non-random as it is been to associate with poor health and negative outcomes such as accelerated cognitive decline in aging researches. Ignoring the informative censoring for disease may lead to erroneous and misleading results. Furthermore, there is substantial evidence that the variability, in addition to the mean, of the markers is associated with disease. Thus it is important to utilize the heterogeneous variance to improve diagnostic accuracy. However, most ROC analyses ignore it which might result in inaccurate or misleading diagnosis. Statistical methods for handling informative censoring and taking advantage of heterogeneous variance in markers in time dependent ROC analysis is lacking. We propose a time-dependent ROC approach that makes use of the heterogeneous variance in markers and takes non-random censoring into account to identify early disease. The effect of aging related genetic polymorphisms (e.g., ApoE4, CETPV405V), and social and behavioral covariates (i.e., perceived stress, personality, depression, and anxiety) will also be examined. We anticipate that our proposed methods, which will be applied to data from the EAS, will guide future efforts to prevent aging related disease by providing more accurate identification of disease onset so that prevention or treatment procedures can be applied more effectively. In addition, these approaches will have broad applicability given that all longitudinal aging studies share similar heterogeneous variance and informative censoring issues.