Methods for Long-Term Follow-Up of HIV-Infected Patients

HIV 感染者的长期随访方法

• 批准号: 7622479
• 负责人: VICTOR GERARD DEGRUTTOLA
• 金额: $ 42.68万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-03-01 至 2011-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7622479
• 关键词: Adherence; Biological Markers; CD4 Lymphocyte Count; Data; Detection; Development; Event; Evolution; Failure; Genetic; Genotype; Goals; HIV; HIV-1; Highly Active Antiretroviral Therapy; Immune response; Infection; Investigation; Joints; Measurement; Measures; Methodology; Methods; Metric; Modeling; Mutation; Nature; Outcome; Outcome Measure; Patients; Pattern; Plasma; Process; RNA; Resistance; Structure; Testing; Time; Treatment outcome; Trees; Uncertainty; Variant; Viral; Viral Load result; Viral load measurement; Virus; base; flexibility; follow-up; immune function; interest; public health relevance; resistance mutation; response; time use; treatment response

DESCRIPTION (provided by applicant): The application describes both parametric and non-parametric approaches to modeling the impact of baseline or time-varying covariates (both low- and high-dimensional) on repeated measures of important biomarker outcomes. Our first aim considers parametric approaches to modeling virological or immunological response to treatment. To be useful, such models must be flexible enough to allow abrupt as well as gradual changes in marker trajectories, and must also incorporate of the impact of factors such as accumulation of resistance mutations, host responses, treatment changes and consequences of co-infections. The models must also accommodate uncertainty in the nature and timing of events, like development of mutations, which cause such changes, as well as frequently missing data. Modeling the effect of resistance is made challenging by the large number of possible mutations and interactions among these mutations, as well as by the presence of multiple clades of virus, large numbers of possible treatments, and the variety of treatment response is measured. Non-parametric methods like CART are available to help reduce the dimensionality of genetic data, and therefore suggest variables for inclusion in parametric models, like those described above. We propose extending CART methodology to allow for both genetic sequences and viral load measurements that are repeated over time, and consider both parametric and non-parametric longitudinal models. Our second aim considers a resampling- based approach to analyze the effect of baseline genetic sequences that is fully nonparametric and allows arbitrary times of measurement. The third aim uses resampling-based methods to test whether variations in the best tree over time are (using the repeated sequences) are consistent with constant underlying relationships between resistance mutations and treatment outcomes, or instead imply that relationships change over time. Our final aim develops non-parametric methods for relating high-dimensional predictors, like HIV genotype or host genetic SNPs, to correlations between responses of interest, possibly with adjustment for other covariates. The goal is to identify predictors of discordance among markers in response to treatment. PUBLIC HEALTH RELEVANCE: The application describes both parametric and non-parametric approaches to describing the impact of baseline or time-varying covariates (both low- and high-dimensional) on repeated measures of important outcomes like viral load or measures of immune function. Challenges arise from the fact that abrupt changes can occur in longitudinal biomarker processes from events like development of resistance mutations whose exact timing is unobservable, as well as from the high dimensionality of the viral genotype and the presence of different types of censoring. Our proposed methods include both highly flexible longitudinal models that accommodate uncertain timing of viral rebound or development of mutations, and non-parametric exploratory methods that accommodate repeated measures of both genotype and viral load; not only does the latter permit investigation of the relationship between patterns of resistance mutations and responses to treatment, but also of the evolution of that relationship over time.

描述（由申请方提供）：本申请描述了用于对基线或时变协变量（低维和高维）对重要生物标志物结局重复测量的影响进行建模的参数和非参数方法。我们的第一个目标是考虑参数方法来模拟病毒学或免疫学对治疗的反应。为了有用，这种模型必须足够灵活，以允许标记轨迹的突然和逐渐变化，并且还必须纳入耐药性突变积累、宿主反应、治疗变化和合并感染后果等因素的影响。模型还必须适应事件性质和时间的不确定性，例如导致这种变化的突变的发展，以及经常丢失的数据。由于存在大量可能的突变和这些突变之间的相互作用，以及存在多个病毒进化枝、大量可能的治疗和治疗反应的多样性，因此对耐药性效应进行建模具有挑战性。CART等非参数方法可用于帮助降低遗传数据的维度，因此建议将变量纳入参数模型中，如上文所述。我们建议扩展CART方法，以允许随着时间的推移重复的基因序列和病毒载量测量，并考虑参数和非参数纵向模型。我们的第二个目标是考虑一个基于resolution的方法来分析基线基因序列的影响，这是完全非参数的，并允许任意时间的测量。第三个目标使用基于重采样的方法来测试最佳树随时间的变化（使用重复序列）是否与耐药突变和治疗结果之间的恒定潜在关系一致，或者相反，意味着关系随时间而变化。我们的最终目标是开发非参数方法，用于将高维预测因子（如HIV基因型或宿主遗传SNP）与感兴趣的响应之间的相关性联系起来，并可能对其他协变量进行调整。目的是确定治疗反应中标记物之间不一致的预测因子。公共卫生相关性：该应用程序描述了参数和非参数方法，用于描述基线或时变协变量（低维和高维）对病毒载量或免疫功能测量等重要结果的重复测量的影响。挑战来自以下事实，即在纵向生物标志物过程中可能发生突变，如发生耐药突变，其确切时间无法观察到，以及病毒基因型的高维性和不同类型的删失。我们提出的方法包括高度灵活的纵向模型，以适应不确定的时间病毒反弹或发展的突变，和非参数探索性方法，以适应基因型和病毒载量的重复测量，后者不仅允许调查耐药突变模式和治疗反应之间的关系，而且还随着时间的推移，这种关系的演变。