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观察数据，这些数据衡量结果的变化和治疗中的变化，其中动态治疗协变量由当前临床指南、通过揭示患者的健康特征或其他因素驱动。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统计方法和软件工具。强调的具体领域是：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。这一目标将开发半参数和非参数统计方法来估计预后评分或标记物准确预测事件时间的能力，其特征是灵敏度和特异度的时间依赖测量。2.观测纵向数据和随时间变化的曝光量。现在，纵向研究常规地收集患者的健康信息和变化的协变量(治疗、暴露)数据。这一目标将开发统计方法，可用于估计暴露的因果影响，并评估标记值指导治疗选择或时机的能力。 公共卫生相关性：在这项提案中，我们将开发新的统计方法来分析纵向数据。特别是，我们将开发能够评估生物标记物的时间依赖的敏感性和特异性的方法，以预测未来的事件时间，如疾病发作或死亡。此外，我们将评估和开发分析电子病历中通常记录的观察性纵向数据的方法，其中健康状况衡量标准和治疗衡量标准都会随着时间的推移而发生变化。我们将重点研究随着时间的推移而修改的治疗的因果效应的估计，以及用于指导选择哪些受试者可能从特定的治疗方案中获得最大好处的生物标记物的能力的估计。

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. PUBLIC HEALTH RELEVANCE: In this proposal, we will develop new statistical methods for the analysis of longitudinal data. In particular, we will develop methods that can evaluate the time-dependent sensitivity and specificity of a biomarker for the prediction of future event times such as disease onset or death. In addition, we will evaluate and develop methods for the analysis of observational longitudinal data commonly recorded in electronic medical records where both measures of health status and measures of treatment change over time. We will focus research on estimation of the causal effect of treatments that are modified over time, and on the estimation of the ability of biomarkers to be used to guide the choice of which subjects are likely to obtain the largest benefit from specific treatment options.