ANALYTIC METHODS FOR HIV TREATMENT AND COFACTOR EFFECTS

我的主要目标是进一步发展新的分析方法。 艾滋病毒感染者的观察性数据库和随机试验。 所建议的方法基于(I)估计新的 因果模型的一类，结构嵌套模型使用了一类新的 估计量，G-估计量，或(Ii)分析半或的新方法 既有信息模型又有非信息模型的非参数模型 信息性缺失的数据。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，新的方法是可用来估计 治疗(如AZT)或辅助因素(如大麻)对 感兴趣的结果(例如，到艾滋病的时间或CD4计数水平) 可获得的观察数据，当艾滋病毒疾病的症状(如鹅口疮， 发烧)同时是混杂因素和中间变量。我们会 使用新的方法来估计治疗和辅助因素对 艾滋病患者外周血中CD_4细胞数量的变化及其与疾病进展的关系 旧金山男性健康研究(SFMHS)的受试者，以及 多中心艾滋病队列研究(MACS)。结果将与 使用标准方法获得的结果。 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，患者在低剂量组中雾化吸入更多的五味子胺 (非随机治疗)。新方法是最好的方法 可用于有效地合并代理标记上的信息 (例如，CD4计数)，以便在尽可能早的时刻停止， 一种治疗对生存时间结局影响的随机试验 (例如，艾滋病的时间)。具体地说，我们需要构造一个有效的阿尔法级别 治疗对生存无影响的零假设检验 结合了关于多变量替代标记进化的数据， 例如(CD4计数和HIV抗原水平)，其功率超过 对数等级检验。我们将使用我们的新方法来进一步分析ACTG试验 0002以及ACTG试验021和081。后两项试验比较 不同化疗药物对机会性鼻咽癌的影响 HIV感染者的感染和生存经历。

My principal aim is further development of new methods for analyzing observational data bases and randomized trials of HIV-infected persons. The proposed approaches are based either on (i) the estimation of a new class of causal models, the structural nested models using a new class of estimators, the G-estimators, or (ii) new methods for analyzing semi- or non-parametric models in the presence of both informative and non- informative missing data. 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 of a treatment (e.g., AZT) or a co-factor (e.g., marijuana) on an outcome of interest (e.g., time to AIDS or CD4-count level) from observational data available, when symptoms of HIV disease (e.g., thrush, fever) are simultaneously confounders and intermediate variables. We shall 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 San Francisco Men's Health Study (SFMHS), and 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 efficiently incorporate information on surrogate markers (e.g., CD4-count) in order to stop, at the earliest possible moment, randomized trials of the effect of a treatment on a survival time outcome (e.g., time to AIDS). Specifically, we to construct a valid alpha-level test of the null hypothesis of no effect of treatment on survival that incorporates data on the evolution of multivariate surrogate markers, e.g., (CD4-count and HIV antigen level), with power exceeding that of the log rank test. We shall use our new methods to further analyze ACTG trial 0002 as well as ACTG trials 021 and 081. These latter two trials compare the effects of various chemotherapeutic agents on the opportunistic infection and survival experience of HIV-infected subjects.