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（由申请人提供）：该申请描述了参数和非参数方法，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果的重复测量的影响进行建模。我们的第一个目标是考虑参数方法来模拟治疗的病毒学或免疫学反应。为了发挥作用，此类模型必须足够灵活，以允许标记物轨迹发生突然和逐渐的变化，并且还必须纳入耐药突变累积、宿主反应、治疗变化和合并感染后果等因素的影响。这些模型还必须适应事件性质和时间的不确定性，例如导致此类变化的突变的发展，以及经常丢失的数据。由于大量可能的突变和这些突变之间的相互作用，以及病毒多个分支的存在、大量可能的治疗方法以及测量的治疗反应的多样性，对耐药性的影响进行建模变得具有挑战性。 CART 等非参数方法可帮助降低遗传数据的维度，因此建议将变量包含在参数模型中，如上所述。我们建议扩展 CART 方法，以允许随时间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。我们的第二个目标考虑采用基于重采样的方法来分析基线基因序列的影响，该方法完全非参数化并允许任意测量次数。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恒定的潜在关系一致，或者暗示关系随着时间的推移而变化。我们的最终目标是开发非参数方法，将高维预测变量（如 HIV 基因型或宿主遗传 SNP）与感兴趣的反应之间的相关性相关联，并可能对其他协变量进行调整。目标是确定治疗反应中标志物之间不一致的预测因素。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）对重复测量重要结果（如病毒载量或免疫功能测量）的影响。挑战源于这样一个事实：纵向生物标志物过程可能会因抗性突变的发生等事件而发生突然变化，这些事件的确切时间是无法观察到的，以及病毒基因型的高维性和不同类型审查的存在。我们提出的方法包括高度灵活的纵向模型（适应病毒反弹或突变发展的不确定时间）和非参数探索方法（适应基因型和病毒载量的重复测量）；后者不仅可以研究耐药突变模式与治疗反应之间的关系，还可以研究这种关系随时间的演变。