Consequences of drug use and informative dropout on HIV/AIDS outcomes in the MACS

描述（由申请人提供）：在前瞻性纵向队列研究中，如多中心艾滋病队列研究（MACS），由于缺少随访或死亡而辍学是很常见的。退出的患者可能更有可能出现疾病进展、使用药物或从事高风险行为。随着时间的推移，队列逐渐倾向于风险较低的健康受试者。因此，在估计药物使用与纵向结果之间的关系时，分析必须考虑受试者损失，否则药物使用的后果将被低估。当退出的概率取决于未观察到的结果时，即使在对可观察数据进行了条件反射之后，缺失数据也不是随机缺失的（MNAR），因此是不可忽略的。尽管有不可忽略的辍学的可能性，传统的方法，如混合或随机效应模型经常被使用。这可能部分是由于现有统计方法的复杂性和无法使用标准软件实现方法。此外，许多调查人员对可能存在的偏见和由此造成的权力损失一无所知。混合模型方法通过将联合结果-辍学分布分解为辍学时间分布f(u)和f(y|u)，即给定辍学结果的分布，来解释辍学机制。得到的完整数据分布f(y)为+f(y|u)dF(u)。参数形式f（y|u）的错误说明可能导致偏差。最近发展的变系数

DESCRIPTION (provided by applicant): Dropout due to loss to follow-up or death is common in prospective longitudinal cohort studies, such as the Multicenter AIDS Cohort Study (MACS). Patients that drop out may be more likely to have disease progression, use drugs or engage in high risk behaviors. Over time, the cohort evolves to be biased towards healthier subjects with lower risks. As a result, when estimating the relationship between drug use and longitudinal outcomes, analyses must consider subject losses or the consequences of drug use will be underestimated. When the probability of dropout depends on the unobserved outcomes, even after conditioning on observable data, the missing data are missing not at random (MNAR) and therefore nonignorable. Despite the likelihood of nonignorable dropout, traditional methods, such as mixed- or random-effects models are frequently used. This may be partially due to the complexity level of existing statistical methods and the inability to implement methods using standard software. In addition, many investigators are na¿ve to possible biases and the resulting loss of power. Mixture model methods account for the dropout mechanism by factoring the joint outcome-dropout distribution into the dropout-time distribution, f(u), and f(y|u), the distribution of the outcome given dropout. The resulting complete data distribution, f(y), is +f(y|u)dF(u). Misspecification of a parametric form of f(y|u) can lead to bias. Recently developed varying-coefficient mixture models can be used to semi-parametrically model the outcome-dropout relationship for a continuous outcome. The method is computationally stable, highly flexible and relatively simple to implement using standard software. A simple extension of this varying-coefficient approach to binary outcomes will be developed. Application of these methods to the MACS data will accurately determine the consequences of drug use on clinical, risk and prevention-oriented outcomes. Varying-coefficient methods that account for dropout will be applied to estimate the relationship between drug use and the clinical outcomes of longitudinal CD4+ T cell count and HIV-1 RNA in untreated subjects (continuous outcomes) and to viral suppression in HAART-treated subjects (a binary outcome). In addition, the influence of drug use on HIV-risk and prevention-oriented outcomes such as high risk sexual risk behavior, HAART- adherence and needle sharing among injection drug users will be examined. PUBLIC HEALTH RELEVANCE: Individuals with disease progression, drug use and other risk behaviors are more likely to drop out of the Multicenter AIDS Cohort Study due to death or loss to follow-up, such that over time, remaining subjects are healthier with lower risks. Analyses exploring the consequences of drug use must therefore account for subject loss. We will utilize new statistical methods to more accurately determine the consequences of drug use on clinical, risk and prevention-oriented outcomes.