Sex, Drugs and Consequences of Dropout on HIV Outcomes in WIHS, MACS, and AIEDRP

描述（由申请人提供）：由于失去随访或死亡而辍学在前瞻性纵向队列研究中很常见，例如妇女跨机构艾滋病毒研究（WIHS），多中心艾滋病队列研究（MACS）和急性感染和早期疾病研究计划（AIEDRP）。退出的患者可能更有可能出现疾病进展，使用药物和酒精或从事高风险行为。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，在估计药物和酒精使用（DAU）与纵向结果之间的关系时，分析必须考虑受试者损失，否则DAU的影响将被低估。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导致的权力损失很天真。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。最近发展的变系数混合模型可以用来半参数地模拟连续结果的结果-退出关系。该方法计算稳定，灵活性高，使用标准软件实现相对简单。我们建议对该方法进行扩展，以适应差异退学和新队列入学的影响；适应非线性关系；并利用马尔可夫链蒙特卡罗方法，通过贝叶斯方法提供了一种更加集成的b样条结选择方法。我们还将开发一个半参数模式混合模型，以适应间歇性缺失的模式。这些创新方法将应用于WIHS、MACS和AIEDRP数据，以按性别准确估计HIV-1感染者活跃DAU的模式；按DAU和性别确定共用针头的预测因素；按性别估计DAU与高活性抗逆转录病毒治疗依从性之间的关系；并估计在治疗和未治疗的HIV-1感染受试者中，DAU与临床HIV结果（CD4+ T细胞计数和HIV-1 RNA）之间的关联。

DESCRIPTION (provided by applicant): Dropout due to loss to follow-up or death is common in prospective longitudinal cohort studies, such as the Women's Interagency HIV Study (WIHS), the Multicenter AIDS Cohort Study (MACS) and the Acute Infection and Early Disease Research Program (AIEDRP). Patients that drop out may be more likely to have disease progression, use drugs and alcohol 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 and alcohol use (DAU) and longitudinal outcomes, analyses must consider subject losses or the impact of DAU 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 naive 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. We propose extensions to this method to accommodate differential dropout and new cohort enrollment effects; accommodate nonlinear relationships; and provide a more integrated method of B-spline knot selection through a Bayesian approach utilizing Markov Chain Monte Carlo methods. We will additionally develop a semi-parametric pattern mixture model to accommodate patterns of intermittent missingness. These innovative methods will be applied to the WIHS, MACS and AIEDRP data to accurately estimate patterns of active DAU in HIV-1 infected subjects by sex; determine predictors of needle sharing by DAU and sex; estimate the relationship between DAU and Highly Active Antiretroviral Therapy adherence by sex; and estimate the association between DAU and clinical HIV outcomes (CD4+ T cell count and HIV-1 RNA) in treated and untreated HIV-1 infected subjects. PUBLIC HEALTH RELEVANCE: Individuals with disease progression, drug use and other risk behaviors are more likely to drop out of the Women's Interagency HIV Study, the Multicenter AIDS Cohort Study and the Acute Infection and Early Disease Research Program due to death or loss to follow-up, such that over time, remaining subjects are healthier with lower risks. Analyses exploring the patterns of drug/alcohol use and the association of drug/alcohol use with HIV outcomes and medication adherence must therefore account for this subject loss. We will utilize new statistical methods to more accurately determine these patterns and associations.