Evaluating predictors of HIV vaccine efficacy: Statistical methods for estimation, testing, and inference

评估 HIV 疫苗功效的预测因素：估计、测试和推断的统计方法

• 批准号: 9769500
• 负责人: Brian David Williamson
• 金额: $ 2.25万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-16 至 2019-12-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769500
• 关键词: Acquired Immunodeficiency Syndrome; Address; Amino Acid Sequence; Amino Acids; Analysis of Variance; Characteristics; Clinical Trials; Complex; Confidence Intervals; Data; Databases; Development; Evaluation; Genotype; Goals; HIV; HIV Infections; HIV prevention trial; HIV vaccine; HIV-1; HIV-1 vaccine; Individual; Infection; Knowledge; Lead; Life; Machine Learning; Measures; Mediating; Methodology; Methods; Modeling; Nested Case-Control Study; Outcome; Participant; Patient Care; Patients; Phase; Positioning Attribute; Predisposition; Prevention; Preventive care; Procedures; Public Health; Research; Research Design; Research Methodology; Research Personnel; Risk; Sampling; Site; Specific qualifier value; Statistical Methods; Supportive care; Techniques; Technology; Testing; Time; Vaccines; Virus; Work; antiretroviral therapy; base; case control; design; efficacy trial; exhaustion; flexibility; interest; neutralizing antibody; predictive modeling; prevent; programs; response; semiparametric; theories; tool; vaccine candidate; vaccine efficacy; vaccine trial; virus characteristic

PROJECT SUMMARY We do not have a broadly efficacious vaccine against HIV, a virus that causes approximately 2 million new infections each year. Current proof-of-concept studies using broadly neutralizing antibodies (bnAbs) against HIV aim to understand how prevention varies with genotypic characteristics of the virus. Since performing an exhaustive search over all genotypic characteristics results in low statistical power to detect effects after adjusting for multiple comparisons, researchers typically pre-specify a small number of features to focus on. There is growing interest in using machine learning-based methods to both corroborate prior understanding and suggest new important genotypic characteristics in predicting sensitivity of the HIV virus to bnAbs. While machine learning-based methods have the potential to yield valid predictive models, issues remain in using these methods for estimating importance. The proposed research will address three such issues: developing a model-free variable importance measure, incorporating information from complex sampling designs, and valid statistical inference both when a genotypic feature is truly important and when it is not. First, the main classical tool for evaluating the importance of characteristics is the ANOVA decomposition, which makes strong modeling assumptions. Machine learning-based methods use minimal assumptions; however, these methods do not generally admit valid statistical inference, and the importance estimates are intimately tied to the technique employed. We will employ an approach based on ideas from the theory of semiparametric estimation and inference to develop a model-free measure of variable importance with valid confidence intervals for the true importance. Second, many HIV vaccine trials incorporate a nested case-control study, where additional information is measured on a subset of the trial participants. Estimating importance only using the subset ignores information from the remaining participants, resulting in a loss of efficiency and potentially adding some bias in estimating variable importance. The proposed research will develop methods that properly account for the sampling design. Finally, to determine if a set of features can be excluded from further analyses, we need a procedure for testing if the feature set truly has no importance. Hypothesis testing using machine learning-based methods is challenging, but we will build on recent advances in semiparametric inference to develop valid procedures for hypothesis testing in the context of variable importance. By combining advances in machine learning technology with ideas from semiparametric estimation and inference, we will determine important feature sets in predicting sensitivity of the HIV virus to bnAbs. In addition to yielding a deeper understanding of HIV neutralization, this information will allow researchers to make the best possible use of data from current clinical trials. This, in turn, could lead to either a shorter time to an HIV vaccine or new bnAbs in the research pipeline that are more broadly efficacious or potent. Any of these outcomes will transform preventative care for patients at risk of HIV infection.

项目概要 我们没有针对艾滋病毒的广泛有效的疫苗，这种病毒导致大约 200 万人新感染艾滋病毒。 每年都有感染。目前的概念验证研究使用广泛中和抗体 (bnAbs) HIV 的目的是了解预防措施如何随病毒的基因型特征而变化。自从执行 对所有基因型特征的详尽搜索导致检测后效果的统计能力较低 为了进行多重比较调整，研究人员通常会预先指定要关注的少量特征。 人们越来越有兴趣使用基于机器学习的方法来证实先前的理解 并提出了预测 HIV 病毒对 bnAb 敏感性的新的重要基因型特征。 虽然基于机器学习的方法有可能产生有效的预测模型，但问题仍然存在 使用这些方法来估计重要性。拟议的研究将解决三个这样的问题： 开发无模型的变量重要性度量，结合来自复杂抽样的信息 当基因型特征真正重要和不重要时，设计和有效的统计推断。第一的， 评估特征重要性的主要经典工具是方差分析（ANOVA），它 做出强有力的建模假设。基于机器学习的方法使用最少的假设；然而， 这些方法通常不承认有效的统计推断，并且重要性估计与 与所采用的技术有关。我们将采用一种基于半参数理论思想的方法 估计和推断，以开发具有有效置信度的变量重要性的无模型度量 真正重要性的间隔。其次，许多艾滋病毒疫苗试验都包含巢式病例对照研究， 其中额外信息是在试验参与者的子集上测量的。仅使用估计重要性 该子集忽略来自其余参与者的信息，导致效率损失并可能 在估计变量重要性时添加一些偏差。拟议的研究将开发适当的方法 考虑抽样设计。最后，确定一组特征是否可以从进一步的排除中排除 分析后，我们需要一个程序来测试功能集是否确实不重要。假设检验使用 基于机器学习的方法具有挑战性，但我们将基于半参数的最新进展 推理以在重要性可变的情况下制定有效的假设检验程序。 通过将机器学习技术的进步与半参数估计和 由此推断，我们将确定预测 HIV 病毒对 bnAb 敏感性的重要特征集。此外 为了更深入地了解 HIV 中和作用，这些信息将使研究人员能够 尽可能利用当前临床试验的数据。反过来，这可能会导致感染艾滋病毒的时间缩短 研究管道中的疫苗或新的 bnAb 更广泛有效或有效。任何这些 结果将改变对有艾滋病毒感染风险的患者的预防性护理。