Methods for high-dimensional data in HIV research

描述(由申请人提供)：我们的研究旨在开发和评估在艾滋病毒研究中分析高维数据的统计方法。现有的大量病毒和宿主特有的生物信息提供了一个令人兴奋的机会，可以根据患者及其感染病毒群体的具体特征做出治疗决定。然而，如何最好地利用这些信息带来了一个分析挑战，因为有大量潜在的相关参数，以及它们之间复杂的、未描述的关系。我们的研究将综合和改进几种分析方法，包括聚类分析、递归划分、混合效应建模、马尔可夫模型和潜在类建模，最终得出延迟临床疾病和死亡的最佳策略。通过开发新的统计方法，我们将利用病毒遗传序列和细胞免疫调节方面的信息，以实现下列特定目标：(1)表征从敏感病毒到耐药病毒的演变以及治疗暴露的中介作用，方法是(1a)结合降维技术和马尔可夫模型，(1b)扩展潜在转移建模框架，以处理在单个时间点属于多个状态的个体，(2)通过(2a)将基于预测的分类扩展到相关数据设置和(2b)扩展潜在类模型以适应状态随时间的变化，来评估细胞免疫调节对CD4计数随时间变化的预测贡献。我们的方法将广泛应用于艾滋病毒/艾滋病研究的几个领域。拟议的研究将包括将我们的方法应用于两个临床数据环境：(1)在3项Efavirenz临床研究期间获得的公开可用病毒遗传学数据集，以及(2)目前正在临床研究中收集的数据子集，该数据集比较艾滋病毒患者的结构化治疗中断和持续治疗。

DESCRIPTION (provided by applicant): Our research aims to develop and evaluate statistical methods for analyzing high-dimensional data in HIV research. The vast array of viral and host specific biological information now available presents an exciting opportunity to tailor treatment decisions to the specific characteristics of patients and their infecting viral populations. However, how best to use this information creates an analytic challenge due to the large number of potentially relevant parameters and the complex, uncharacterized relationships among them. Our research will integrate and advance several analytic methods including cluster analysis, recursive partitioning, mixed effects modeling, Markov modeling and latent class modeling to arrive ultimately at the best strategies for delaying clinical disease and death. Through the development of novel statistical methods, we will draw from information on viral genetic sequences and cellular immune modulation to achieve the following specific aims: (1) To characterize the progression from sensitive to resistant virus over time and the mediating role of treatment exposure through (1a) combining dimension reduction techniques and Markov models and (1b) extending the latent transition modeling framework to handle an individual belonging to multiple states at a single time point and (2) To assess the predictive contribution of cellular immune modulation on changes in CD4 count over time through (2a) extending prediction based classification to the correlated data setting and (2b) extending the latent class model to accommodate changes in state over time. Our methods will apply broadly to several areas of HIV/AIDS research. The proposed research will include the application of our methods to two clinical data settings: (1) a publicly available viral genetics dataset obtained during 3 clinical studies of Efavirenz and (2) a subset of data currently being collected in a clinical study comparing structured treatment interruption to continuous therapy in HIV patients.