Statistical Methods for Ordinal Variables in HIV/AIDS Studies

HIV/AIDS 研究中顺序变量的统计方法

• 批准号: 9268842
• 负责人: Chun Li
• 金额: $ 39.25万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-18 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9268842
• 关键词: Statistical; Methods; Ordinal; Variables; HIV

DESCRIPTION (provided by applicant): Many important variables in biomedical studies of HIV/AIDS are ordered categorical. A few examples include WHO clinical stage, level of education, frequency of douching, stage of cervical lesions, self- reported condom use, and biallelic genotypes. Although ordinal variables are common, statistical methods that account for their ordered nature are lacking, particularly when the ordinal variable is a predictor variable. Most standard methods either treat the ordinal predictor as categorical (ignoring the order information) or continuous (making linearity assumptions). This proposal develops statistical methods that account for the ordered nature of ordinal variables without making linearity assumptions. The methods address situations when a predictor variable (X) is ordered categorical; the outcome variable (Y) is continuous, discrete, counts, time-to-event, or repeated measures; and there are multiple covariates (Z). The general approach is to fit appropriate regression models of Y on Z, and X on Z, and then to test for correlation between the residuals from these two models. The methods therefore rely on a new definition of residual for ordered categorical data. Statistical properties of this residual are evaluated, as well as its use in model diagnostics. Asymptotic properties of the residual- based test statistics are computed, relationships with other methods are derived, user-friendly software that implements these methods is developed, and the advantages of the new methods are studied using simulated and real data. The methods are applied to two HIV studies: The first assesses the effect of the frequency of douching on sexually transmitted infections among a cohort of adolescent females using marginal structural models. The second data application looks for human genetic polymorphisms associated with drug plasma levels, virologic failure, and toxicities for patients initiating an efavirenz- or abacavir-based antiretroviral regimen.

描述（由申请人提供）：许多重要的变量在生物医学研究的艾滋病毒/艾滋病是有序的分类。一些例子包括WHO临床分期、教育水平、冲洗频率、宫颈病变分期、自我报告的避孕套使用情况和双等位基因基因型。虽然有序变量是常见的，但缺乏解释其有序性的统计方法，特别是当有序变量是预测变量时。大多数标准方法将有序预测因子视为分类（忽略顺序信息）或连续（进行线性假设）。该提案开发了统计方法，该方法解释了有序变量的有序性，而无需进行线性假设。该方法解决了预测变量（X）为分类排序变量;结局变量（Y）为连续变量、离散变量、计数变量、至事件时间变量或重复测量变量;以及存在多个协变量（Z）的情况。一般方法是拟合Y对Z和X对Z的适当回归模型，然后检验这两个模型的残差之间的相关性。因此，该方法依赖于一个新的定义的残差有序分类数据。该残差的统计特性进行评估，以及其在模型诊断中的使用。计算基于残差的检验统计量的渐近性质，导出与其他方法的关系，开发实现这些方法的用户友好的软件，并使用模拟和真实的数据研究新方法的优点。该方法适用于两个艾滋病毒研究：第一个评估的影响，冲洗的频率对性传播感染的一个队列的青春期女性使用边际结构模型。第二个数据应用程序寻找与药物血浆水平相关的人类遗传多态性，病毒学失败，以及启动基于依法韦仑或阿巴卡韦的抗逆转录病毒治疗方案的患者的毒性。