Statistical Methods for HIV/AIDS Studies

这项拟议的研究旨在解决几乎每一项临床研究中出现的重要分析问题 终点是某一临床或生物事件发生的时间(例如，死亡时间、艾滋病、HIV-RNA 失败等)。 特别是，该项目建议实现以下具体目标： 1.发展基于聚合的模型和变量选择技术，具有临床意义 二元、连续和删失事件结果回归模型中的预测误差 2.研究昂贵或侵入性预测标记物附加值的评估方法 在艾滋病毒和其他传染病研究中使用预测的结果或诊断疾病 目标1中制定和研究的标准； 3.开发新的生存敏感性分析工具，在有信息的情况下进行分析 审查；以及 4.给出了一类非比例生存模型估计的有效组合 具有截尾失效时间数据的风险半参数模型。 与公共卫生的相关性在过去15年中，艾滋病毒研究取得了重要进展， 从而增加了艾滋病毒感染者的治疗选择。越来越详细的 艾滋病毒的生物测量，如病毒的基因突变或患者基因组中的信息， 现在不仅提供了估计人口影响的机会，而且还提供了开发 预测个别患者的结果，包括治疗的潜在毒副作用、生物反应或 临床或生物学失败的时间。这项研究的结果有望帮助临床医生提供 为患者提供更好的护理和病例管理。

The proposed research aims to solve important analytical problems arising in almost every clinical study, in which the endpoint is the time to a certain clinical or biological event (e.g., the time to death, AIDS, HIV-RNA failure, etc.). In particular, this project proposes to address the following specific aims: 1. To develop model and variable selection techniques based on aggregate, clinically-meaningful prediction error in regression models for binary, continuous, and censored event outcomes; 2. To study methods for assessing the added value of expensive or invasive markers for predicting outcome or diagnosing disease in HIV and other infectious disease studies using the prediction criteria developed and studied in Aim 1; 3. To develop new sensitivity analysis tools for survival, analysis in the presence of informative censoring; and 4. To develop efficient combinations of a class of estimates for survival models for non-proportional hazards semi-parametric models with censored failure time data. Relevance To Public Health Important progress has been made in the last 15 years in HIV research, resulting in an increase in the treatment options for HIV-infected individuals. The increasingly detailed biologic measurements in HIV,such as genetic mutations in the virus or information in a patient's genome, now provide the opportunity not only for estimating population effects, but also for developing models for predicting individual patient outcomes, including potential toxic side effects of therapy, biologic response, or the time to clinical or biologic failure. The results of this research are expected to assist clinicians in providing better care and case management for patients.