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

描述（由申请人提供）：该申请描述了对基线或时变协变量（低维和高维）对重要生物标志物结果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化方法来模拟对治疗的病毒学或免疫反应。为了发挥作用，这些模型必须足够灵活，以允许标记轨迹的突然和逐渐变化，并且还必须纳入诸如耐药突变积累、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的性质和时间的不确定性，比如导致这种变化的突变的发展，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，以及存在多个病毒分支、大量可能的治疗方法和测量治疗反应的多样性，对耐药性的影响进行建模具有挑战性。像CART这样的非参数方法可以帮助降低遗传数据的维数，因此建议将变量包含在参数模型中，就像上面描述的那样。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了一种基于重采样的方法来分析完全非参数和允许任意测量时间的基线基因序列的影响。第三个目标是使用基于重新采样的方法来测试随着时间的推移，最佳树的变化是否（使用重复序列）与耐药性突变和治疗结果之间的恒定潜在关系一致，或者相反，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，将高维预测因子（如HIV基因型或宿主遗传snp）与感兴趣的反应之间的相关性联系起来，可能需要调整其他协变量。目的是确定对治疗反应的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来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。挑战来自这样一个事实，即纵向生物标志物过程中可能发生突变，如耐药突变的发生，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审查的存在。我们提出的方法包括高度灵活的纵向模型，以适应病毒反弹或突变发展的不确定时间，以及非参数探索方法，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与治疗反应之间的关系，而且还允许调查这种关系随时间的演变。

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.