ANALYTIC METHODS FOR HIV TREATMENT AND COFACTOR EFFECTS

描述（改编自摘要）：本报告的主要目标 应用是新分析方法的进一步发展 HIV 感染者的观察数据库和随机试验。 的 所提出的方法基于（1）对新类别的估计 因果模型，或（2）分析半参数或非参数的新方法 存在信息性和非信息性缺失数据的模型。 新类别的因果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数据 因素，即结果的风险因素也可以预测随后的结果 使用正在研究的药物或辅助因子进行治疗。 所提出的方法 分析将通过以下方式改进以前的方法： 首先，新方法是可用于估计效果的最佳方法 o 针对以下结果的治疗（例如 AZT）或辅助因子（例如大麻） 观察数据的兴趣（例如，达到艾滋病或 HIV RNA 水平的时间）， 当 HIV 疾病症状（例如鹅口疮、发烧）同时出现时 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 进化影响的方法 受试者中艾滋病毒疾病进展的计数和时间 多中心艾滋病队列研究 (MACS)。 结果将与结果进行比较 使用标准方法获得。 其次，新方法是可用于调整的最佳方法 依赖性审查、非随机违规、治疗交叉或 终止，以及额外的非随机的并发效果 随机临床试验中的治疗。 例如，在ACTG审判002中 高剂量与低剂量AZT对艾滋病患者生存的影响 患者中，低剂量组的患者服用了更多的雾化喷他脒（a 非随机治疗）。 新方法是可用的最佳方法 有效整合替代标记物的信息（例如 HIV RNA） 为了尽早停止随机试验 治疗对生存时间结果的影响（例如，患艾滋病的时间）。 具体来说，研究人员将构建一个有效的 A-level 测试 合并数据的治疗对生存率没有影响的零假设 关于多变量替代标记（例如 CD4 计数和 HIV RNA），其功效超过对数秩检验。 他们应使用 新方法进一步分析 ACTG 试验 002 以及 ACTG 试验 021 和 175. 这最后两项试验是对不同方法效果的比较。 化疗药物对机会性感染和生存的影响 HIV感染者的经历。

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.