Methods to find and model predictors of the causal effect of HAART

HAART 因果效应的寻找和模型预测方法

• 批准号: 8446289
• 负责人: Judith Jacqueline Lok
• 金额: $ 30.36万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-19 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8446289
• 关键词: AIDS clinical trial group; AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Acute; Address; Affect; Anti-Retroviral Agents; CD4 Lymphocyte Count; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Clinical; Collaborations; Computer software; Data; Databases; Dependence; Disease; Drops; Drug resistance; Early Diagnosis; Early treatment; Enrollment; Event; Future; Guidelines; HIV; HIV Infections; HIV Seropositivity; HIV-1; Hospitals; Immune; Immune system; Individual; Infection; Investigation; Journals; Lead; Life; Measures; Methodology; Methods; Modeling; Outcome; Outcome Measure; Patients; Persons; Phase; Probability; Process; Public Health; Randomized; Randomized Clinical Trials; Research; Research Personnel; Residual state; ST7 gene; Sampling; Specific qualifier value; Staging; Statistical Methods; Testing; Time; Toxic effect; Treatment outcome; Uncertainty; Viral Load result; Weight; base; follow-up; immune activation; improved; interest; novel; outcome forecast; programs; reconstitution; reconstruction; standard of care; theories; treatment duration; treatment effect; treatment strategy

DESCRIPTION (provided by applicant): This project develops and applies new causal inference statistical methodology to investigate: (1) the impact of the time of initiation of Highl Active Anti-Retroviral Treatment (HAART) following infection on short- and long-term immune reconstruction in the beginning stages of HIV-1 infection, (2) the impact of treatment regimes that initiate HAART once CD4 count drops below a certain threshold, possibly depending on viral load, and (3) the impact of immune activation and CD4 counts one year since HAART initiation on long-term beneficial clinical outcomes had all patients remained on suppressive HAART. The first two aims will inform the clinical decision process of when to initiate HAART. Delaying HAART initiation has the advantage of postponing the adverse toxicities from HAART and drug resistance, and hence might improve long-term prognoses. However, delaying initiation can lead to irreversible immune system damage. This major issue in public health is especially relevant in the early phases of HIV-infection, for which no firm treatment guidelines exist, and even more so in view of the current CDC efforts to encourage early diagnosis of HIV-infection. The third aim is important because it can help decide whom to target with new therapies besides HAART and inform studies of the effect of such new therapies. For all three aims, randomized clinical trials may be unethical, since they would entail forcing patients to continue being off or on treatment over prolonged periods of time after baseline. Therefore, the field has to base these treatment strategies on analyses of observational data, where the treatment decisions are not randomized, and sicker patients are more likely to initiate treatment earlier and discontinue treatment later. Thus, estimation methods must adequately adjust for the patient characteristics that confound the relationship between times of initiating treatment and the outcome of interest. This proposal outlines new causal inference methods based on a new class of Structural Nested Mean Models and a new type of Inverse Probability of Censoring Weighting to adjust for this confounding by indication, since previous methods are not directly applicable to our settings. These new methods generate a large class of estimators, and naive choices of estimators are inefficient in that they lead to large standard errors. Thus, with those naive choices, models with many parameters encoding the dependence of the treatment effect on current covariates cannot well be estimated in samples of reasonable size. This proposal addresses many unresolved issues in this methodology and its applications. The objectives of this proposal are three-fold. The first objective is to develop accurate and precise estimators, using theory about semiparametric models, to be submitted to methodological journals. The second objective is to apply these methods, the results of which will be submitted to journals about HIV/AIDS. And the third objective is to make the resulting software publicly available for researchers in HIV/AIDS and other fields. These methods may have a great impact on other investigations, since time-dependent confounding by treatment indication is a very common problem that leads to bias in the absence of proper statistical methods like the ones proposed here.

描述（由申请人提供）：该项目开发并应用了新的因果推理统计方法来调查：（1）感染后高升高活性抗逆转录病毒治疗（HAART）对短期和长期免疫的影响在HIV-1感染的开始阶段的重建，（2）一旦CD4计数降至一定阈值以下，启动HAART的治疗方案的影响可能取决于病毒负荷，以及（3）免疫激活和CD4的影响。自哈特（Haart）对长期有益临床结局的启动以来，所有患者仍在抑制HAART上。前两个目标将为何时启动Haart的临床决策过程提供信息。延迟HAART的启动具有推迟HAART和耐药性的不良毒性的优势，因此可以改善长期预后。但是，延迟启动会导致不可逆的免疫系统损害。公共卫生中的这一主要问题在HIV感染的早期阶段尤其重要，艾滋病毒感染的早期阶段不存在公司治疗指南，甚至鉴于当前的CDC努力鼓励早期诊断HIV感染的努力。第三个目标很重要，因为它可以帮助决定谁针对HAART以外的新疗法，并了解此类新疗法的影响。 对于这三个目标，随机临床试验可能是不道德的，因为它们会迫使患者在基线后长时间的时间内继续接受或接受治疗。因此，该领域必须将这些治疗策略基于观察数据的分析，而观察数据的分析不是随机的，并且患者更可能更早地开始治疗并稍后停止治疗。因此，估计方法必须充分调整患者特征，这些患者特征会混淆启动治疗时间与感兴趣结果之间的关系。该提案根据新的结构嵌套均值模型和一种新型的反相反概率概述了新的因果推理方法，以审查加权以根据指示调整这种混杂性，因为以前的方法不直接适用于我们的设置。 这些新方法会产生大量的估计器，而估计器的天真选择效率低下，因为它们会导致大量标准错误。因此，有了那些天真的选择，在合理大小的样本中，无法很好地估计具有许多参数编码治疗效应对当前协变量的模型。该提案解决了该方法及其应用中的许多未解决的问题。该提案的目标是三倍。第一个目标是使用有关半参数模型的理论来开发准确，精确的估计量，将其提交给方法学期刊。第二个目标是应用这些方法，其结果将提交给有关艾滋病毒/艾滋病的期刊。第三个目标是使生成的软件可公开用于艾滋病毒/艾滋病和其他领域的研究人员。这些方法可能会对其他研究产生很大的影响，因为治疗指示的时间依赖性混杂是一个非常普遍的问题，它在没有适当的统计方法的情况下会导致偏见。