Modeling HIV Subjects' Electronic Monitoring Device Data

对 HIV 受试者的电子监测设备数据进行建模

• 批准号: 6796110
• 负责人: GEORGE J KNAFL
• 金额: $ 23.95万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6796110
• 关键词: HIV infections; behavioral /social science research tag; clinical research; data collection methodology /evaluation; human data; mathematical model; outcomes research; patient monitoring device; statistics /biometry; therapy compliance

DESCRIPTION (provided by applicant): Adherence to HIV medications is especially important for preventing partial suppression of viral replication with its enhanced risk of drug resistant HIV and is increasingly being measured in clinical trials with electronic monitoring devices (EMDs). EMD data are rich in longitudinal information often not used to their maximum potential. Summary measures are most commonly used, but do not provide sufficient detail for describing complex medication-taking patterns. We recently developed alternate methods for modeling EMD data at both the individual- and multiple-subject levels providing new insights into adherence patterns and evaluated these methods using MEMS cap data from a clinical trial testing the effectiveness of a nursing intervention for improving adherence to HIV medications. These methods utilize adaptive Poisson regression modeling of grouped EMD data with likelihood cross-validation for model evaluation and rule-based heuristic search through parametric models generating a smooth nonparametric regression fit. We now propose to develop original statistical methods extending adaptive Poisson regression for the purpose of improving its usefulness to HIV researchers and clinicians by addressing the following specific aims: 1) Identify subperiods within the EMD observation period over which a subject or group of subjects exhibits distinctly different adherence patterns. 2) Identify the dependence on time of variability in adherence for a subject or group of subjects. 3) Identify classes of subjects with distinctly different adherence across those classes and similar adherence within those classes for evaluation of possible differential effects of an intervention across those classes as well as the impact of such adherence classes on clinical outcomes. To accomplish these aims, we will develop algorithms for adaptively determining subperiods of distinctly different adherence patterns and for relating these changes to known changes in subjects' treatment and experience; for incorporating changes in variability in adherence using nonparametric quasi-likelihood methods; and for adaptively determining I parsimonious classifications of subjects for predicting change in adherence and its effect on clinical outcomes and for assessing how much of such change can be attributed to specific known factors especially intervention group membership. We will evaluate these methods, using available EMD data for HIV subjects, to assess their usefulness in the understanding, treatment, and prevention of HIV disease/AIDS.

描述（由申请人提供）：遵守HIV药物对于防止其耐药性HIV的风险增强的部分抑制病毒复制尤为重要，并且在通过电子监测设备（EMDS）的临床试验中越来越多地测量。 EMD数据富含纵向信息，通常不用于其最大潜力。摘要措施是最常用的，但没有提供足够的细节来描述复杂的药物治疗模式。我们最近开发了在个体和多个受试者水平上对EMD数据进行建模的替代方法，从而提供了对依从性模式的新见解，并使用临床试验中的MEMS CAP数据评估了这些方法，从而测试了护理干预措施改善依从性HIV药物的有效性。这些方法利用具有可能性交叉验证的分组的EMD数据的自适应泊松回归模型，通过参数模型产生平滑的非参数回归拟合度，用于模型评估和基于规则的启发式搜索。现在，我们建议开发扩展自适应泊松回归的原始统计方法，目的是通过解决以下具体目的来改善其对HIV研究人员和临床医生的有用性：1）确定在EMD观察期内的子周期，主题或组对象在该期间内表现出明显不同的依附模式。 2）确定对受试者或受试者组的依从性变异性时间的依赖性。 3）确定在这些类别之间具有明显不同依从性的受试者类别，并且在这些类别中的依从性相似，以评估这些类别的干预措施的可能差异效应，以及此类粘附类别对临床结果的影响。为了实现这些目标，我们将开发算法，以自适应确定截然不同的依从性模式的子周期，并将这些变化与受试者治疗和经验的已知变化联系起来；使用非参数准样方法纳入依从性变化的变化；并且为了自适应地确定对受试者的简化分类，以预测依从性的变化及其对临床结果的影响，并评估有多少此类变化可以归因于特定的已知因素，尤其是干预组成员资格。我们将使用可用的EMD数据来评估这些方法，以评估其在理解，治疗和预防HIV疾病/艾滋病方面的有用性。