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

项目总结/摘要 在前瞻性纵向队列研究中，因失访或死亡而脱落很常见，例如 妇女机构间艾滋病研究（WIHS）、多中心艾滋病队列研究（MACS）和急性感染 早期疾病研究计划（AIEDRP）退出的患者可能更有可能患有疾病 进展，使用药物和酒精或从事高风险行为。随着时间的推移，这一群体逐渐演变成 偏向于风险较低的健康受试者因此，在估计药物与 和酒精使用（DAU）和纵向结果，分析必须考虑受试者损失或DAU的影响 会被低估。当脱落的概率取决于未观察到的结果时，即使在 在可观察数据的条件下，缺失数据是非随机缺失（MNAR），因此 不可否认的尽管有可能出现不可否认的辍学，传统的方法，如混合或随机， 经常使用效果模型。这可能部分是由于现有统计数据的复杂程度。 方法和无法使用标准软件实现方法。此外，许多调查人员 对可能的偏见和由此造成的权力损失一无所知。 混合模型方法通过分解联合结果-辍学分布来解释辍学机制 到脱落时间分布，f（u）和f（y| u），给定辍学率的结果分布。所得 完全数据分布，f（y），is +f（y| u）dF（u）。f（y）的一个参数形式的误指定|（1）可能导致偏见。 最近开发的变系数混合物模型可以用来半参数模型的结果- dropout关系for a continuous连续outcome结果.该方法计算稳定，高度灵活， 使用标准软件实现相对简单。我们建议扩展此方法， 适应不同的辍学和新的队列招募效果;适应非线性关系; 并通过利用马尔可夫的贝叶斯方法提供了更集成的B样条节点选择方法 链蒙特卡罗方法。我们还将开发一个半参数模式混合模型， 适应间歇性失踪的模式这些创新方法将应用于WIHS， MACS和AIEDRP数据，以准确估计按性别分列的HIV-1感染受试者的活动性DAU模式; 通过DAU和性别确定共用针头的预测因素;估计DAU和高度 按性别分列的主动抗逆转录病毒治疗依从性;以及估计DAU与临床HIV之间的关联 治疗和未治疗的HIV-1感染受试者的结局（CD 4 + T细胞计数和HIV-1 RNA）。

Project Summary / Abstract 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 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. 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.