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，由于失去随访或死亡而辍学是很常见的，例如多中心艾滋病队列研究(MACS)。辍学的患者可能更有可能出现疾病进展、使用药物或从事高危行为。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，在估计药物使用和纵向结果之间的关系时，分析必须考虑受试者的损失，否则药物使用的后果将被低估。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。新近发展起来的变系数 混合模型可用于对连续结果的结果--退出关系进行半参数建模。该方法计算稳定，灵活性高，用标准软件实现相对简单。将对这种变系数方法进行简单的扩展，使之适用于两种结果。将这些方法应用于MACS数据将准确地确定药物使用对临床、风险和预防结果的影响。考虑到辍学的变系数方法将被用来估计药物使用与未经治疗的受试者的纵向CD4+T细胞计数和HIV-1RNA的临床结果(连续结果)以及接受HAART治疗的受试者的病毒抑制(二元结果)之间的关系。此外，还将审查药物使用对艾滋病毒风险和预防结果的影响，如高危性风险行为、遵守HAART和注射吸毒者共用针头。公共卫生相关性：有疾病进展、吸毒和其他危险行为的人更有可能因死亡或失去随访而退出多中心艾滋病队列研究，这样随着时间的推移，剩余的受试者更健康，风险更低。因此，探索吸毒后果的分析必须考虑到受试者的损失。我们将利用新的统计方法，更准确地确定药物使用对临床、风险和预防结果的影响。

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.