Analytic Methods for HIV Treatment and Co-factor Effects

这项建议的主要目的是进一步发展分析艾滋病毒感染者观察性研究和随机试验的新方法。所提出的方法要么基于(i)对新类别因果模型的估计，要么基于（ii）在存在信息和非信息缺失数据的情况下分析半参数或非参数模型的新方法。新的因果模型包括结构嵌套模型、边际结构模型、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来说是“流行病学”的，因为它们需要时间相关的混杂因素的数据，即影响结果的风险因素，这些因素也可以预测药物或研究中的辅助因素的后续治疗。所提出的分析方法将在以下方面改进以前的方法。首先，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时，这些新方法是根据观察数据估计治疗（如HAART）或辅助因素（如大麻）对感兴趣的结果（如到艾滋病的时间或艾滋病毒RNA水平）的总体（净）或直接影响的最佳方法。我们将使用这些新方法来评估多中心艾滋病队列研究（MACS）受试者中cd4计数演变和hiv疾病进展时间的治疗和辅助因素效应，加州合作伙伴研究中不一致合作伙伴之间的传播，以及ACTG试验219中蛋白酶抑制剂对hiv感染儿童生长发育的影响。结果将与用标准方法得到的结果进行比较。其次，在随机临床试验中，新方法是调整依赖性审查、非随机不依从性、治疗交叉或终止以及其他非随机治疗并发效应的最佳方法。例如，在ACTG试验002中，高剂量AZT与低剂量AZT对艾滋病患者生存的影响，低剂量组患者服用更多雾化喷他脒（一种非随机治疗）。新方法是现有的最佳方法，可以有效地结合替代标记物（如HIV RNA）的信息，以便尽早停止治疗对生存时间结果（如艾滋病时间）影响的随机试验，同时保留治疗对生存没有影响的零假设的有效α水平检验。我们将使用我们的新方法来分析ACTG试验002、021、175、A5057、343、371和384的子集。

The principal aim of this proposal is further development of new methods for analyzing observational studies and randomized trials of HIV-infected persons. The proposed approaches are based either on (i) the estimation of new classes of causal models, or (ii) 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 overall (net) or direct effect of a treatment (e.g., HAART) 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. We shall use the new methods to estimate treatment and co-factor effects of the evolution of CD4-counts and on time to progression of HIV-disease among subjects in the Multicenter AIDS Cohort Study (MACS), transmission between discordant partners in the California Partners' Study, and the effect of protease inhibitors on the growth and development of HIV-infected children in pediatric ACTG Trial 219. 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., HIV RNA) 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), while preseving a valid alpha-level test of the null hypothesis of no effect of treatment on survival. We shall use our new methods to analyze a subset of ACTG trials 002, 021, 175, A5057 rollover, 343, 371, and 384.