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.

项目摘要/摘要 2015年，世界卫生组织（WHO）引入了所有艾滋病毒患者的待遇指南， 建议在诊断后立即开始抗逆转录病毒疗法（ART）治疗 严重程度。从那时起，全球大多数国家都采用了该政策。但是，理解 这种政策非常有限，尤其是关于艾滋病毒疾病的进展。专注于事件历史结果 （以WHO临床阶段和死亡为代表），我们最近进行了初步分析。我们使用了来自 国际流行病学数据库的中部非洲地区评估艾滋病（CA-EIDEEA） 基于目标试验设计的模型（在政策之后建造了两个同类群 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 由于失去随访或转移，不太可能为所有审查的个人持有。另外，样本相对较小 Ca-iDea的尺寸阻碍了我们的能力1）探索更多临床相关和生物学上合理的模型 对于艾滋病毒疾病的进展和2）探索有关治疗的影响的人口异质性 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 利用多区域，即全球 - 欧洲数据，该数据将提供大大更大的样本。我们将 制定程序，以解决目标试验下多层模型的信息（依赖）审查 设计以进行灵敏度分析。例如，我们提出参数，非参数和半参数 随机处理审查的方法。此外，我们提供了一种受控的多个插补方法来处理 审查不是随机的。我们将使用内部和外部数据比较和验证这些方法。最后， 我们将全面分析全球数据数据，在该数据中，灵敏度分析将确保 我们的发现。拟议的工作将通过提供有关可能的更多详细信息来提高艾滋病毒护理研究 艾滋病毒疾病进展的进化课程和改变了所有人的有效性的因素。我们的分析 是开发更精确的患者治疗选择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建模其他进化的疾病 通过预定义的临床状态，具有间歇性数据收集模式的临床状态受相似的数据复杂性。