Statistical Methods for Longitudinal Studies

描述(由申请人提供)：由于测量和数据库技术的不断进步，医学纵向研究面临着新的分析挑战。具体地说，分子分析、医学成像和心理评估方面的创新已经产生了许多新的疾病进展假定标记物。此外，电子数据记录的进步现在允许纵向调查收集以精细时间分辨率测量的高维结果数据。该提案的总体目标是开发用于分析现代纵向生物医学数据的回归方法、图形摘要和软件工具。 具体的重点领域包括： 1.重复测量和随时间变化的准确性。生物标记物是表征患者健康状况的特定方面的测量。对于临床事件时间T，生物标记物数据的分析将关注给定标记物的当前值P[T i oA；T I Y i oA(S)]的预测存活分布，其中Y i oA(S)表示在时间S(或其历史的函数)处测量的生物标记物，以及由事件时间的标记物分布的特征定义的生物标记物的时间相关准确性P[Y i ota(S)&gt；c]T i iota=t]。这一目标将开发半参数统计方法来估计协变量特定的纵向预测值和纵向准确性，其特征是敏感性和特异性。 2.纵向分类数据与似然推理。现在，纵向研究经常在大量的时间点收集患者的健康信息。例如，空气污染对健康影响的观察性研究，以及过敏症状的药效学研究。在这些例子中的每一个例子中，每天的分类结果数据被记录了几个月，在此期间环境暴露(污染、花粉)也不同。这一目标将为分类纵向数据开发灵活的基于似然的估计方法。

DESCRIPTION (provided by applicant): Longitudinal studies in medicine are faced with new analysis challenges due to continually advancing measurement and database technologies. Specifically, innovations in molecular assays, medical imaging, and psychological assessment have generated numerous new putative markers of disease progression. Also, advances in electronic data recording now allow longitudinal investigations to collect high-dimensional outcome data measured at a fine time resolution. The overall goals of this proposal are to develop regression methodology, graphical summaries, and software tools for analyzing modern longitudinal biomedical data. The specific areas of emphasis are: 1. Repeated measures and time-dependent accuracy. Biomarkers are measurements that characterize specific aspects of patient health status. With a clinical event time, T iota, analysis of biomarker data will focus both on the predictive survived distribution given the current value of the marker, P[T iota> T I Y iota(s)] where Y iota(s) represents the measured biomarker at time s (or a function of its history), and on the time-dependent accuracy of the biomarker as defined by characteristics of the marker distribution conditional on event time, P[Y iota(s) > c ] T iota = t]. This aim will develop semi-parametric statistical methods to estimate covariate specific longitudinal predictive values and longitudinal accuracy as characterized by measures of sensitivity and specificity. 2. Longitudinal categorical data and likelihood inference. Longitudinal studies now routinely collect patient health information at a large number of time points. Examples include observational studies of the health effects of air pollution, and pharmacodynamic studies of allergy symptoms. In each of these examples daily categorical outcome data is recorded for several months during which ambient exposure (pollution, pollen) also varies. This aim will develop flexible likelihood-based estimation methods for categorical longitudinal data.