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。具体来说，在分子分析、医学成像和心理评估方面的创新产生了许多新的疾病进展的假定标记。此外，电子数据记录的进步现在允许纵向调查收集观察性数据，测量结果的变化和治疗的变化，其中动态治疗协变量由当前临床指南驱动，通过揭示患者健康特征或其他因素。本建议的总体目标是发展统计方法和软件工具来分析现代纵向生物医学数据。具体的重点领域是：1。重复测量和随时间变化的准确性。生物标志物是表征患者健康状况的特定方面的测量。该目标将开发半参数和非参数统计方法，以估计预后评分或标记物准确预测事件时间的能力，其特征是依赖于时间的敏感性和特异性措施。2. 观测纵向数据和时间依赖性暴露。现在，纵向研究常规地收集患者健康信息和变化的协变量（治疗、暴露）数据。这一目标将发展统计方法，可用于估计暴露的因果效应，并评估标志物值指导治疗选择或时机的能力。

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 observational data measuring changes in outcomes and changes in treatments where dynamic treatment covariates are driven by current clinical guidelines, by unfolding patient health characteristics, or other factors. The overall goals of this proposal are to develop statistical methodology 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. This aim will develop semi-parametric and non-parametric statistical methods to estimate the ability of prognostic scores or markers to accurately predict event times as characterized by time-dependent measures of sensitivity and specificity. 2. Observational longitudinal data and time-dependent exposure. Longitudinal studies now routinely collect both patient health information and changing covariate (treatment, exposure) data. This aim will develop statistical methods that can be used to estimate causal effects of exposure, and to evaluate the ability of marker values to guide the choice or timing of treatment.