Detecting early disease using variability in markers under informative censoring

在信息审查下利用标记物的变异性检测早期疾病

• 批准号: 9141689
• 负责人: Cuiling Wang
• 金额: $ 3.64万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9141689
• 关键词: Detecting; early; disease; using; variability

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.

描述（由申请人提供）：检测早期疾病，例如，阿尔茨海默病（AD），所有引起痴呆、虚弱、制度化等，是发展 预防或延迟疾病的干预措施，是许多衰老研究的重要目标。对于纵向研究，时间依赖性受试者工作特征（ROC）分析，它整合了ROC分析中的时间维度，是一种有用且强大的方法，可用于评估标记物在不同随访时间内预测发病率的预后能力。然而，大多数常用模型的内在假设是删失过程是随机的，由于死亡而进行删失违反了这一假设，因为死亡与疾病相关。由于辍学而进行的删失通常也是非随机的，因为它与健康状况不佳和负面结果有关，例如老龄化研究中的认知能力加速下降。忽视疾病信息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 标记物的平均值与疾病相关。因此，利用异质性方差来提高诊断准确性是重要的。然而，大多数ROC分析忽略了它，这可能导致不准确或误导的诊断。缺乏处理信息性删失和利用时间依赖性ROC分析中标记物异质性方差的统计方法。我们提出了一种时间依赖的ROC方法，该方法利用标记物的异质性方差，并考虑到非随机删失来识别早期疾病。衰老相关的遗传多态性的影响（例如，ApoE4，CETPV405V），以及社会和行为协变量（即，知觉压力、个性、抑郁和焦虑）也将被检查。我们预计，我们提出的方法，这将适用于EAS的数据，将指导未来的努力，以防止衰老相关的疾病，提供更准确的识别疾病发作，使预防或治疗程序可以更有效地应用。此外，这些方法将具有广泛的适用性，因为所有纵向老化研究都有类似的异质性方差和信息删失问题。