Statistical Methods for HIV/AIDS Studies

描述(申请人提供)：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，在这些研究中，终点是某个临床或生物事件的时间(例如，死亡时间、艾滋病、艾滋病毒-RNA失败等)。具体地说，本项目建议解决以下具体目标：1.开发基于二元、连续和删失事件结果回归模型中具有临床意义的聚合预测误差的模型和变量选择技术；2.研究在HIV和其他传染病研究中使用在目标1中开发和研究的预测标准来评估昂贵或侵入性标记物用于预测结果或诊断疾病的附加值的方法；3.开发新的生存敏感性分析工具，在存在信息审查的情况下进行分析；以及4.开发非比例风险半参数模型与删失失效时间数据的一类估计的有效组合。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，导致艾滋病毒感染者的治疗选择增加。艾滋病毒中日益详细的生物测量，如病毒的基因突变或患者基因组中的信息，现在不仅为估计总体影响提供了机会，也为开发预测单个患者结果的模型提供了机会，包括治疗的潜在毒副作用、生物反应或临床或生物失败的时间。这项研究的结果有望帮助临床医生为患者提供更好的护理和病例管理。

DESCRIPTION (provided by applicant): 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.