ANALYTIC METHODS FOR HIV TREATMENT AND COFACTOR EFFECTS

描述(改编自摘要)：本报告的主要目的 应用是分析新方法的进一步发展 艾滋病毒感染者的观察性数据库和随机试验。这个 所提出的方法基于(1)对新类别的估计 因果模型，或(2)半参数或非参数分析的新方法 在存在信息性和非信息性缺失数据的情况下的模型。 因果模型的新类别包括结构嵌套模型、边际模型 结构模型、直接影响结构嵌套模型和连续 时间结构嵌套模型。许多新方法从根本上说是 “流行病学”，因为他们需要与时间相关的混淆数据 因素，也就是也预测后续结果的风险因素 使用正在研究的药物或辅助因子进行治疗。建议的方法 分析将在以下方面改进以前的方法： 首先，新方法是评估效果的最佳方法 O治疗(如AZT)或辅助因素(如大麻)对以下结果的影响 兴趣(例如，艾滋病或艾滋病毒RNA水平的时间)来自观测数据， 当HIV疾病的症状(如鹅口疮、发烧)同时出现时 混杂因素和中间变量。研究人员将使用新的 估计治疗和辅助因素对CD4演变的影响的方法 中国受试者中HIV-疾病的计数和进展时间 多中心艾滋病队列研究(MACS)。结果将与结果进行比较 使用标准方法获得。 其次，新方法是可供调整的最佳方法 依赖审查、非随机不遵守、治疗交叉或 终止，以及额外的非随机化的同时影响 随机临床试验中的治疗。例如，在ACTG试验002中， 大剂量与小剂量AZT对艾滋病患者生存的影响 患者，低剂量组的患者雾化吸入更多的五烷胺(a 非随机治疗)。新方法是可用的最好的方法 有效地合并有关代理标记(例如艾滋病毒RNA)的信息 为了尽快停止随机试验， 治疗对生存时间结果的影响(例如，艾滋病发生的时间)。 具体地说，研究人员将构建一个有效的a-Level测试 纳入数据的治疗对存活率无影响的零假设 多变量替代标志物(如CD4计数和HIV)的研究进展 RNA)，其威力超过对数秩检验法。他们应使用 分析进一步的ACTG试验002以及ACTG试验021和 175.这最后两项试验是对不同药物效果的比较 化疗药物对机会性感染与生存的影响 艾滋病病毒感染者的经验。

DESCRIPTION (adapted from the Abstract): The principal aim of this application is the further development of new methods for analyzing observational data bases and randomized trials of HIV-infected persons. The proposed approaches are based either on (1) the estimation of new classes of causal models, or (2) new methods for analyzing semi- or non-parametric models in the presence of both informative and non-informative missing data. The new classes of causal models include structural nested models, marginal structural models, direct effect structural nested models, and continuous time structural nested models. Many of the new methods are fundamentally "epidemiologic" in that they require data on time-dependent confounding factors, that is, risk factors for outcomes that also predict subsequent treatment with the drug or co-factor under study. The proposed methods of analysis will improve upon previous methods in the following ways: First, the new methods are the best methods available to estimate the effect o a treatment (e.g., AZT) or a co-factor (e.g., marijuana) on an outcome of interest (e.g., time to AIDS or HIV RNA levels) from observational data, when symptoms of HIV disease (e.g., thrush, fever) are simultaneously confounders and intermediate variables. The researchers will use the new methods to estimate treatment and co-factor effects on the evolution of CD4 counts and on time to progression of HIV-disease among subjects in the Multicenter AIDS Cohort Study (MACS). Results will be compared with results obtained using standard methods. Second, the new methods are the best methods available to adjust for dependent censoring, non-random non-compliance, treatment cross-over or termination, and the concurrent effect of additional non-randomized treatments in randomized clinical trials. For example, in ACTG Trial 002 of the effect of high-dose versus low-dose AZT on the survival of AIDS patients, patients in the low-dose arm took more aerosolized pentamidine (a non-randomized treatment). The new methods are the best methods available to incorporate information efficiently on surrogate markers (e.g., HIV RNA) in order to stop, at the earliest possibl moment, randomized trials of the effect of a treatment on a survival time outcome (e.g., time to AIDS). Specifically, the researchers will construct a valid a-level test of the null hypothesis of no effect of treatment on surviva that incorporates data on the evolution of multivariate surrogate markers (e.g., CD4-count and HIV RNA), with power exceeding that of the log rank test. They shall use the new methods to analyze further ACTG Trial 002 as well as ACTG Trials 021 and 175. These last two trials are comparisons of the effects of various chemotherapeutic agents on the opportunistic infection and survival experience of HIV-infected patients.