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的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)新的因果模型的估计，或者(Ii)在同时存在信息性和非信息性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。许多新方法基本上是“流行病学”的，因为它们需要与时间相关的混杂因素的数据，也就是结果的风险因素，也就是预测随后使用研究中的药物或辅助因素进行治疗的风险因素。拟议的分析方法将在以下方面改进以前的方法。首先，当HIV疾病的症状(例如鹅口疮、发烧)同时是混杂因素和中间变量时，新方法是从观察数据中估计治疗(例如，HAART)或辅助因素(例如，大麻)对感兴趣的结果(例如，艾滋病发生的时间或HIV RNA水平)的总体(净)或直接影响的最佳方法。在多中心艾滋病队列研究(MACS)的受试者中，我们将使用新的方法来估计治疗和辅助因素的影响，包括在多中心艾滋病队列研究(MACS)的受试者中，在HIV疾病的进展时间上；在加利福尼亚伙伴研究中，在不和谐的伙伴之间传播；以及在儿科ACTG试验219中，估计蛋白酶抑制剂对艾滋病毒感染儿童生长和发育的影响。结果将与使用标准方法获得的结果进行比较。其次，在随机临床试验中，新方法是可用于调整依赖审查、非随机不依从性、治疗交叉或终止以及附加非随机治疗的同时效应的最佳方法。例如，在大剂量与低剂量AZT对艾滋病患者存活率影响的ACTG试验002中，低剂量组的患者服用更多的喷雾剂喷雾剂(一种非随机治疗)。新方法是最有效地合并有关替代标记物(例如，HIV RNA)的信息的最佳方法，以便在尽可能早的时刻停止治疗对生存时间结果(例如，艾滋病的时间)的影响的随机试验，同时对治疗对生存没有影响的无效假设进行有效的阿尔法水平检验。我们将使用我们的新方法来分析ACTG试验002、021、175、A5057翻转、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.