Statistical Methods for HIV/AIDS Studies

HIV/艾滋病研究的统计方法

• 批准号: 8106419
• 负责人: LEE-JEN WEI
• 金额: $ 28.14万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8106419
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Characteristics; Clinical; Clinical Trials; Communicable Diseases; Data; Disease; Disease Management; Event; Funding; Future; HIV; HIV Infections; Health; Highly Active Antiretroviral Therapy; Individual; Joints; Medicine; Methodology; Methods; Modeling; Monitor; Neurologic; Observational Study; Outcome; Patients; Procedures; Randomized; Reaction Time; Research; Research Personnel; Risk; Safety; Statistical Methods; Subgroup; Survival Analysis; Time; Universities; base; comparative; design; novel; response; sound; standard care; treatment strategy

DESCRIPTION (provided by applicant): Great efforts have been made over the past 20 years in HIV-related research. The results have provided useful information regarding the patient's disease management based on the individual patient characteristics. However, most of such "personalized medicine" strategies were obtained on an ad hoc basis. There are numerous examples of subgroup analyses that could not be validated. Novel quantitative methods are needed to systematically develop sound treatment strategies at a personal level. For example, on average, the highly active antiretroviral therapy (HAART) is a very potent therapy for treating HIV-infection. However, not everyone benefits from HAART. Using data from clinical trials and observational studies, it is important to establish prediction models that can identify future patients who would benefit from such potent therapies. One of the main themes for this current research plan is to systematically develop quantitative methods to make valid inferences for future patient responses. There are five research aims in the proposal. The first three specifically address the personalized medicine issues. The last two aims are to develop new methods for designing, monitoring, and analyzing HIV studies with event time responses, which are commonly used endpoints for evaluating new therapies. The PI has assembled a research team which has been very productive in developing theoretically sound and practical quantitative methods for designing, monitoring and analyzing HIV- related clinical trials and observational studies over the past funding periods. The PI and co-investigators are also senior statisticians for the Center for Biostatical AIDS Research Center at Harvard University. They are intimately involved in HIV studies via the AIDS Clinical Trails Group (ACTG) and Neurologic AIDS research Consortium (NARC). They have access to the data from past and current ACTG and NARC studies, and can directly apply the results from developed methodologies to the on-going and future HIV studies. PUBLIC HEALTH RELEVANCE: Quantitative methods will be developed for personalized medicine approaches for patient management of HIV disease using the data from clinical trials and observational studies. New statistical procedures will also be developed for designing, monitoring, and analyzing clinical trials for evaluating new therapies for HIV-infection.

描述（申请人提供）：过去20年来，我们在艾滋病毒相关研究方面做出了巨大努力。结果提供了有用的信息，关于病人的疾病管理的基础上，个别病人的特点。然而，大多数这种“个性化医疗”策略是在临时基础上获得的。有许多亚组分析的例子无法验证。需要新的定量方法来系统地制定个人水平的合理治疗策略。例如，平均而言，高效抗逆转录病毒疗法（HAART）是治疗HIV感染的非常有效的疗法。然而，并不是每个人都能从HAART中受益。利用来自临床试验和观察性研究的数据，建立预测模型非常重要，这些模型可以识别未来将从这些有效疗法中受益的患者。当前研究计划的主要主题之一是系统地开发定量方法，为未来患者的反应做出有效的推断。本研究提出了五个研究目标。前三个具体解决个性化医疗问题。最后两个目标是开发新的方法，用于设计，监测和分析具有事件时间响应的HIV研究，这些研究是评估新疗法的常用终点。PI组建了一个研究团队，在过去的资助期间，该团队在开发理论上合理和实用的定量方法方面非常富有成效，用于设计，监测和分析与艾滋病毒相关的临床试验和观察性研究。PI和共同研究者也是哈佛大学生物统计艾滋病研究中心的高级统计学家。他们通过艾滋病临床试验组（ACTG）和神经系统艾滋病研究联盟（NARC）密切参与艾滋病毒研究。他们可以访问过去和当前ACTG和NARC研究的数据，并可以直接将开发方法的结果应用于正在进行和未来的艾滋病毒研究。公共卫生关系：将利用临床试验和观察性研究的数据，为艾滋病毒疾病患者管理的个性化医疗方法开发定量方法。还将开发新的统计程序，用于设计、监测和分析临床试验，以评估艾滋病毒感染的新疗法。

项目成果

Allele-specific amplification in cancer revealed by SNP array analysis.

• DOI: 10.1371/journal.pcbi.0010065
• 发表时间: 2005-11
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: LaFramboise T;Weir BA;Zhao X;Beroukhim R;Li C;Harrington D;Sellers WR;Meyerson M
• 通讯作者: Meyerson M

Reducing bias in parameter estimates from stepwise regression in proportional hazards regression with right-censored data.

使用右删失数据减少比例风险回归中逐步回归的参数估计偏差。

• DOI: 10.1007/s10985-007-9078-5
• 发表时间: 2008
• 期刊: Lifetime data analysis
• 影响因子: 1.3
• 作者: Soh,Chang-Heok;Harrington,DavidP;Zaslavsky,AlanM
• 通讯作者: Zaslavsky,AlanM