STATISTICAL ISSUES IN AIDS RESEARCH

该项目将在以下几个主要领域采用统计方法 艾滋病研究。 围绕艾滋病危机的紧迫感导致有必要 实现更有效和高效的设计和分析的方法 临床试验。减少进行以下操作所需的时间和资源 研究，成组序列设计的多变量扩展将是 使用开发的计数处理工具，并长期代理角色 将对长期临床疗效措施进行调查。新的强健 误测或遗漏协变量分析的非参数方法 我们将探索数据。 HIV流行率估计和艾滋病流行参数预测 增进对疫情动态的了解，并提供 为公共卫生规划预测未来走向的基础 目的。对流行率的准确估计将通过开发 通过使用对混合样品进行分析的统计方法 聚合酶链式反应测定法和小面积估计法 技巧。一种新的基于伪似然的统计方法 将开发用于估计各种参数的函数 艾滋病流行，当人们使用艾滋病毒流行数据时， 其他数据来源的参数估计。所有建议的方法 将在实际的艾滋病研究数据集中实施。 ARC和艾滋病患者入院时的临床和心理过程 医疗保健系统可能相当复杂。灵活统计 将探索方法论，以便更好地理解 本课程通过对登记处和#年产生的数据进行分析 临床试验。特别令人感兴趣的是多变量 允许时间依赖协变量的过程模型。 有特别令人信服的理由来获得方法来 确保艾滋病机密研究和患者护理数据的安全。 将开发两种概率数据安全方法，基于 哈希码标识方法和部分信息文件匹配 方法：研究方法。

This project will pursue statistical methods in several main areas of AIDS research. The sense of urgency surrounding the AIDS crisis has led to the need for methods enabling more effective and efficient design and analysis of clinical trials. To reduce the time and resources required to conduct studies, multivariate extensions of group sequential designs will be developed using counting process tools, and the role surrogates for long term clinical efficacy measures will be investigated. New robust nonparametric methods for analysis of mismeasured or missing covariate data will be explored. Estimation of HIV prevalence and projection of AIDS epidemic parameters improves the understanding of the dynamics of the epidemic and provides a basis for predicting its future course for public health planning purposes. Precise estimates of prevalence will be obtained by developing statistical methods for assays performed on pooled samples, through use of Polymerase Chain Reaction Assays, and by using small area estimation techniques. Novel statistical methods based on a pseudo-likelihood function will be developed for the estimation of various parameters of the AIDS epidemic, when one uses HIV prevalence data augmented by parameter estimates from other sources of data. All proposed methods will be implemented in actual AIDS research data sets. The clinical and psychological course for ARc and AIDS patients entering the health care system can be quite complex. Flexible statistical methodology will be explored which will allow a better understanding of this course through the analysis of data arising in registries and in clinical trails. Of particular interest will be multivariate point process models which allow time dependent covariates. There are particularly compelling reasons to obtain approaches to insuring security of confidential AIDS research and patient care data. Two probabilistic data security approaches will be developed, based on hash code identifier methods and partial information file matching methods.