ANALYTIC METHODS FOR HIV TREATMENT AND COFACTOR EFFECTS

描述（改编自摘要）：本文件的主要目的 应用是新的分析方法的进一步发展 观察数据库和艾滋病毒感染者的随机试验。 的 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 模型中存在的信息和非信息缺失数据。 新的因果关系模型包括结构嵌套模型、边际模型、 结构模型、直接效应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 “流行病学”，因为它们需要时间依赖性混杂的数据 也就是说，风险因素的结果，也预测随后的 用研究中的药物或辅因子治疗。 建议的方法 分析将在以下方面改进以前的方法： 首先，新方法是估计影响的最佳方法 o治疗（例如，AZT）或辅因子（例如，大麻）的结果 兴趣（例如，时间艾滋病或艾滋病毒RNA水平）从观察数据， 当HIV疾病的症状（例如，鹅口疮，发烧）同时 混杂因素和中间变量。 研究人员将使用新的 评估治疗和辅助因子对CD4演变的影响的方法 受试者中的HIV计数和至疾病进展时间 多中心艾滋病队列研究（MACS）。 将结果与结果进行比较 使用标准方法获得。 其次，新方法是可供调整的最佳方法 相关删失、非随机不依从、治疗交叉或 终止，以及额外的非随机化 随机临床试验中的治疗。 例如，在ACTG试验002中， 高剂量与低剂量AZT对艾滋病患者存活率的影响 低剂量组的患者使用了更多的喷他脒（a 非随机化治疗）。 这些新方法是现有的最佳方法 为了有效地结合关于替代标记的信息（例如，HIV RNA） 为了尽早停止随机试验， 治疗对存活时间结果的影响（例如，艾滋病时间）。 具体来说，研究人员将构建一个有效的a级测试， 包含数据的治疗对生存无影响的零假设 关于多变量替代标记的演变（例如，CD4计数和HIV RNA），其功效超过了对数秩检验的功效。 他们将使用 进一步分析ACTG试验002以及ACTG试验021和 175. 这最后两个试验是比较各种 化疗药物对机会性感染和生存的影响 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.