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

项目总结/摘要 2015年，世界卫生组织（世卫组织）推出了针对艾滋病毒感染者的“全方位治疗”指南， 建议在诊断后立即开始抗逆转录病毒治疗（ART），无论疾病如何 严重性。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 这种政策相当有限，特别是在艾滋病毒疾病进展方面。关注事件历史结局 （以WHO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 评估艾滋病国际流行病学数据库（CA-IEDEA）的中部非洲区域， 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 收养）。这项工作说明了几个局限性。例如，无信息删失的假设是 由于失访或转出，不太可能对所有删失个体保持。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合理模型的能力 2）探索关于Treat-All对HIV疾病进展的影响的人群异质性， 结果。在拟议的研究中，我们计划通过开发新的统计方法来解决这些局限性， 利用多区域，即，全球IEDEA数据，这将提供一个更大的样本。我们将 制定程序，以解决目标试验下多状态模型的信息（依赖）删失问题 设计允许进行敏感性分析。例如，我们提出了参数，非参数和半参数 处理随机删失的方法。此外，我们还提供了一种受控的多重插补方法来处理 不是随机的我们将使用内部和外部数据比较和验证这些方法。最后， 我们将全面分析全球IeDEA数据，其中敏感性分析将确保 我们的发现这项拟议的工作将通过提供有关可能的艾滋病毒治疗的更详细信息， HIV疾病进展的演变过程和改变Treat-All有效性的因素。我们的分析 是朝着制定更精确的患者治疗方案和资源分配迈出的第一步， 患者结局。所提出的统计方法也可以应用于模拟其他疾病的演变 通过具有间歇性数据收集模式的预定义临床状态，该模式受到类似数据复杂性的影响。

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.