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

根据艾滋病毒疾病进展和政策评估的数据复杂性，开发事件历史数据的统计方法

• 批准号: 10700452
• 负责人: Denis Nash
• 金额: $ 26.08万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-16 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700452
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adopted; Adoption; African; Binding; CD4 Lymphocyte Count; Caring; Cells; Central Africa; Cessation of life; Characteristics; Clinic; Clinical; Country; Data; Data Collection; Databases; Development; Diagnosis; Disease; Disease Progression; Disease model; Effectiveness; Eligibility Determination; Enrollment; Ensure; Epidemic; Epidemiology; Evaluation; Event; Evolution; Future; Guidelines; HIV; HIV/AIDS; Individual; International; Intervention; Investigation; Laboratories; Methods; Modeling; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Policies; Policy Analysis; Policy Developments; Population Heterogeneity; Probability; Procedures; Prognostic Factor; Randomized, Controlled Trials; Recommendation; Recording of previous events; Research; Research Personnel; Resource Allocation; Sample Size; Sampling; Selection Bias; Severity of illness; Statistical Methods; Structure; Testing; Time; Validation; Withdrawing Care; Work; World Health Organization; antiretroviral therapy; clinically relevant; cohort; data complexity; demographics; design; follow-up; hazard; improved; mortality; outreach; semiparametric; trial design

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.

项目概要/摘要 2015 年，世界卫生组织 (WHO) 推出了针对艾滋病毒感染者的“Treat-All”指南，该指南 建议诊断后立即开始抗逆转录病毒疗法（ART）治疗，无论疾病如何 严重程度。此后，世界上大多数国家都采取了这一政策。然而，对影响的理解 此类政策相当有限，特别是在艾滋病毒疾病进展方面。关注事件历史结果 （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 中部非洲地区多州艾滋病评估国际流行病学数据库 (CA-IeDEA) 基于目标试验设计的模型（其中构建了两个队列，一组在政策实施之前，一组在政策实施之后） 采用）。这项工作阐明了一些局限性。例如，非信息审查的假设是 由于失去后续行动或转出，不太可能适用于所有受审查的个人。而且样本相对较小 CA-IeDEA 的规模阻碍了我们 1) 探索更多临床相关性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和 利用多区域数据，即全球 IeDEA 数据，这将提供更大的样本。我们将 制定程序来解决目标试验下多状态模型的信息（依赖）审查问题 设计允许进行敏感性分析。例如，我们提出参数、非参数和半参数 处理随机审查的方法。此外，我们提供了一种受控多重插补方法来处理 审查不是随机的。我们将使用内部和外部数据来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数据的稳健性 我们的发现。拟议的工作将通过提供有关可能的更详细信息来推进艾滋病毒护理研究 HIV 疾病进展的进化过程以及改变 Treat-All 有效性的因素。我们的分析 是开发更精确的患者治疗方案和资源分配的第一步，从而改善 患者的结果。所提出的统计方法也可能应用于模拟其他进化疾病 通过具有类似数据复杂性的间歇性数据收集模式的预定义临床状态。