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

描述(由申请人提供)：在前瞻性纵向队列研究中，由于失去随访或死亡而辍学是很常见的，例如妇女机构间艾滋病毒研究(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)。辍学的患者可能更有可能出现疾病进展，使用药物和酒精，或从事高危行为。随着时间的推移，这群人逐渐倾向于风险更低、更健康的受试者。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的损失，否则DAU的影响将被低估。当辍学的概率取决于未观察到的结果时，即使在以可观察到的数据为条件之后，丢失的数据也不是随机丢失的(MANAR)，因此不可忽略。尽管有不可忽视的辍学可能，但传统方法，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程度，以及无法使用标准软件执行方法。此外，许多调查人员对可能的偏见和由此而失去的权力感到天真。混合模型方法通过将联合结果-丢弃分布分解为丢弃时间分布f(U)和给定丢弃的结果的分布f(y|u)来解释丢弃机制。得到的完整数据分布f(Y)为+f(y|u)df(U)。对f(y|u)的参数形式的错误指定可能会导致偏差。最近发展起来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-丢弃关系。该方法计算稳定，灵活性高，用标准软件实现相对简单。我们提出了对该方法的扩展，以适应不同的退学和新的队列登记效应；适应非线性关系；并通过使用马尔可夫链蒙特卡罗方法通过贝叶斯方法提供了更完整的B-Spline节点选择方法。我们还将开发一个半参数模式混合模型来适应间歇性缺失的模式。这些创新方法将被应用于WIHS、MACS和AIEDRP数据，以准确地估计HIV-1感染者按性别划分的活跃DAU模式；确定DAU和性别共用针头的预测因素；按性别估计DAU与高效抗逆转录病毒治疗依从性之间的关系；以及估计DAU与HIV-1感染者的临床结果(CD4+T细胞计数和HIV-1RNA)之间的关联。 公共卫生相关性：有疾病进展、药物使用和其他危险行为的人更有可能因死亡或失去后续行动而退出妇女机构间艾滋病毒研究、多中心艾滋病队列研究和急性感染和早期疾病研究计划，这样随着时间的推移，剩余的受试者更健康，风险更低。因此，探讨药物/酒精使用模式以及药物/酒精使用与艾滋病毒结果和服药依从性之间的关系的分析必须考虑到这一主题的损失。我们将利用新的统计方法更准确地确定这些模式和关联。

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.