Developing Statistical Methods on Event History Data Subject to Data Complexities for HIV Disease Progression and Policy Evaluation

项目摘要/摘要 2015年，世界卫生组织(WHO)为艾滋病毒携带者推出了治疗所有人的指南，其中 建议在确诊后立即开始抗逆转录病毒疗法(ART)治疗，而不考虑疾病 严肃性。从那时起，世界上大多数国家都采取了这一政策。然而，对影响的理解 这种政策是相当有限的，特别是关于艾滋病毒疾病的进展。关注事件历史结果 (以世卫组织临床分期和死亡为代表)，我们最近进行了初步分析。我们使用的数据来自 多国艾滋病评估国际流行病学数据库(CA-IeDEA)中非区域 基于目标试验设计的模型(其中构建了两个队列，一个在政策之前，一个在政策之后 领养)。这项工作阐明了几个局限性。例如，非信息性审查的假设是 由于失去跟踪或调出，不太可能适用于所有被审查的个人。另外，相对较小的样本 CA-IeDEA的大小阻碍了我们1)探索更多临床相关和生物学上可信的模型的能力 对于HIV疾病的进展和2)探索种群异质性关于治疗-All对 结果。在拟议的研究中，我们计划通过开发新的统计方法和 利用多区域数据，即全球IeDEA数据，这将提供大得多的样本。我们会 制定程序以解决目标试验下多状态模型的信息(依赖)审查问题 设计时要考虑到敏感度分析。例如，我们提出了参数、非参数和半参数 处理随机审查的方法。此外，我们还提供了一种受控的多重归责方法来处理 审查不是随意的。我们将使用内部和外部数据对这些方法进行比较和验证。最后， 我们将全面分析全球IeDEA数据，其中的敏感度分析将确保 我们的发现。拟议的工作将通过提供更详细的信息来促进艾滋病毒护理方面的研究 艾滋病毒疾病进展的进化过程和改变通用疗法有效性的因素。我们的分析 是朝着开发更精确的患者治疗方案和资源分配迈出的第一步，从而改善 病人的结果。提出的统计方法也可能应用于对其他进化的疾病进行建模 通过具有类似数据复杂性的间歇性数据收集方案的预定义临床状态。

PROJECT SUMMARY/ABSTRACT In 2015, the World Health Organization (WHO) introduced Treat-All guidelines for people living with HIV, which recommend immediate initiation of antiretroviral therapy (ART) treatment upon diagnosis regardless of disease severity. Since then, most countries worldwide have adopted the policy. However, the understanding of the impact of such policy is quite limited, especially regarding HIV disease progression. Focused on event history outcome (represented by WHO clinical stages and death), we recently conducted a preliminary analysis. We used data from the Central Africa region of the International epidemiology Database to Evaluate AIDS (CA-IeDEA) for a multistate model based on a target trial design (where two cohorts were constructed, one before and one after the policy adoption). This work illuminated several limitations. For example, the assumption of non-informative censoring was unlikely to hold for all censored individuals due to loss of follow-up or transfer out. Also, the relatively small sample size of the CA-IeDEA hindered our capacities to 1) explore more clinically relevant and biologically plausible models for HIV disease progression and 2) explore population heterogeneities regarding the impact of the Treat-All on the outcome. In the proposed study, we plan to address these limitations by developing new statistical methods and leveraging the multi-regional, i.e., the global-IeDEA data, which will provide a substantially larger sample. We will develop procedures to address informative (dependent) censoring for the multistate models under the target trial design to allow for sensitivity analysis. For example, we propose parametric, nonparametric, and semi-parametric approaches to handle censoring at random. In addition, we offer a controlled multiple imputation method to handle censoring not at random. We will compare and validate those methods using both internal and external data. Finally, we will comprehensively analyze the global-IeDEA data, where the sensitivity analysis will ensure the robustness of our findings. The proposed work will advance research in HIV care by providing more detailed information on possible evolutionary courses of HIV disease progression and factors that modify the effectiveness of Treat-All. Our analysis is a first step towards developing more precise patient treatment options and resource allocation, thereby improving patient outcomes. The proposed statistical methods may also have applications to model other diseases that evolve through predefined clinical states with intermittent data collection schema subject to similar data complexities.