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

HIV 感染者的长期随访方法

基本信息

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
项目类别：
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

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

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项目摘要

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）：该申请描述了对基线或随时间变化的协变量（低维和高维度）对重要生物标志物结果的重复测量的参数和非参数方法。我们的第一个目标考虑了对治疗的病毒学或免疫学反应进行建模的参数方法。为了有用，这样的模型必须足够灵活，以使标记轨迹的突然以及逐渐变化，并且还必须纳入因素的影响，例如抗药性突变的积累，宿主反应，治疗变化和共同感染的后果。这些模型还必须适应事件的性质和时机的不确定性，例如突变的发展，这些变化以及经常缺少数据。通过这些突变之间的大量可能突变和相互作用以及存在多种病毒，大量可能的治疗方法以及测量了多种治疗反应的种类，对抗药性的影响进行了挑战。可以使用诸如CART之类的非参数方法来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。我们提出扩展的CART方法，以允许随着时间的推移重复进行遗传序列和病毒载荷测量，并考虑参数和非参数纵向模型。我们的第二个目标考虑了一种基于重新采样的方法来分析完全非参数并允许任意测量时间的基线遗传序列的影响。第三目的使用基于重新采样的方法来测试最佳树中最佳树的变化（使用重复序列）是否与电阻突变和治疗结果之间的恒定潜在关系一致，或者暗示关系随着时间的推移而变化。我们的最终目的开发了将高维预测因子（如HIV基因型或宿主遗传SNP）关联到感兴趣的响应之间的相关性的非参数方法，并可能与其他协变量的调整有关。目的是确定标志物在响应治疗方面的不一致的预测指标。公共卫生相关性：该应用程序描述了参数和非参数方法来描述基线或时变协变量（低维和高维）对重复衡量病毒载荷或免疫功能量度等重要结果的措施的影响。挑战是由于纵向生物标志物过程中可能发生突然变化的事件而引起的挑战，这些事件是诸如抗性突变的发展，其确切的时机无法观察到，以及病毒基因型的高维度以及不同类型的检查的高维度。我们提出的方法包括高度灵活的纵向模型，可容纳病毒反弹或突变发展的不确定时机，以及非参数探索方法，可容纳基因型和病毒负荷的重复测量。后者不仅允许研究抵抗突变模式与对治疗的反应之间的关系，而且还允许随着时间的流逝而演变。