Analytic Methods for HIV Treatment and Co-factor Effects

该提案的主要目的是进一步发展新的方法，用于分析艾滋病毒感染者的观察性研究和随机试验。 所提出的方法是基于（i）估计的新类别的因果模型，或（ii）新的方法，用于分析半或非参数模型的信息和非信息缺失数据的存在。 新的因果关系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，因为它们需要关于时间依赖性混杂因素的数据，即，也预测随后使用所研究的药物或辅因子进行治疗的结果的风险因素。 所提出的分析方法将在以下方面改进以前的方法。首先，新方法是可用于估计治疗的总体（净）或直接效果的最佳方法（例如，HAART）或辅因子（例如，大麻）对感兴趣的结果（例如，时间艾滋病或艾滋病毒RNA水平）从观察数据，当症状的艾滋病毒疾病（例如，鹅口疮、发热）同时是混杂因素和中间变量。 我们将使用新方法评估多中心艾滋病队列研究（MACS）受试者中CD 4计数演变和至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伴侣之间的传播，以及儿科ACTG试验219中蛋白酶抑制剂对HIV感染儿童生长发育的影响。 将结果与使用标准方法获得的结果进行比较。其次，新方法是调整相关删失、非随机不依从、治疗交叉或终止以及随机临床试验中额外非随机治疗的并发效应的最佳方法。 例如，在ACTG试验002中，研究了高剂量与低剂量AZT对AIDS患者生存率的影响，低剂量组的患者服用了更多雾化喷他脒（非随机治疗）。 这些新方法是有效结合替代标志物信息的最佳方法（例如，HIV RNA），以便尽早停止治疗对生存时间结果影响的随机试验（例如，时间艾滋病），同时保持一个有效的α水平检验的无效假设，治疗对生存没有影响。 我们将使用我们的新方法分析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.