Drug and Comorbid Trajectories: Jointly-Modeled, Differently-Distributed Outcomes

描述（由申请人提供）：了解多种药物成瘾之间的相互关系以及共同发生的物质使用和精神疾病之间的关系是NIDA的一个关键优先事项，因为大量研究显示其并发症的发生率极高。现有分析方法的局限性阻碍了这些领域的进一步进展，因为描述药物使用和并发疾病随时间变化的轨迹是复杂的-通常是非线性的，并且基于具有不同分布的结果。时间轴追踪（TLFB）是用于确定药物使用结果的最广泛使用和接受的措施，收集在指定时间间隔内对多种药物的日常使用报告（例如，使用大麻，是/否;每天关节）。但TLFB数据未按收集时进行分析（例如，重复二项式和泊松变量）。相反，数据通常被折叠成总结，如试验期间的总使用天数（或戒断），或折叠成较小的间隔（例如每月使用天数的总和），以试图创建正态分布的变量。这样的综合得分牺牲了信息，失去了效率，而且往往是不正常的。存在大量先前收集的关于具有不同分布的结局的纵向数据，如TLFB的数据，但无法对它们进行分析，从而无法令人满意地解决科学和临床相关问题，例如：使用两种或多种药物之间的时间关系是什么？药物使用的变化与合并疾病的变化之间的时间关系是什么？同时出现的精神症状是否随着药物使用的减少而缓解，或者精神症状的减少先于药物使用的减少？在什么情况下，一个相对于另一个发生减少？本计画的前两个目标是提出严谨的理论推导与模拟，以发展多元非线性混合模式（MvNLMIXED）同时估计和比较物质使用和合并症结果的非线性轨迹随时间和组间的不同分布（目标1），并通过估计它们的关联（目标2a）和顺序来评估这些联合建模轨迹之间的相互关系，一个人与另一个人之间的时间差（目标2b）。目标3和4将应用MvNLMIXED方法回答两项药物治疗试验中的重要问题，这两项试验是针对同时接受认知行为治疗以使用药物的青少年中合并发生的注意力缺陷多动障碍（ADHD）：用途：在NIDA临床试验网络中进行的一项托莫西汀试验和一项渗透释放哌甲酯多中心试验。这些新的分析预计将确定不同药物相互使用（目标3）和ADHD（目标4）之间的重要相互关系，可能为治疗提供信息。除了对于评估这两项试验以及一般药物使用和合并症之间的相互关系的重要性之外，这些新方法还广泛适用于评估任何联合建模的纵向变量之间的时间关系，使新研究受益并增强现有数据库的科学价值。将在多个平台上提供用于这些方法的方便用户的软件。

DESCRIPTION (provided by applicant): Understanding the inter-relationships among addiction to multiple drugs and between co-occurring substance use and psychiatric disorders is a key priority of NIDA's, given ample research showing extremely high rates of their co-morbidity. Limitations of existing analytic methods impede further progress in these areas, because trajectories describing drug use and co-occurring disorders over time are complex--often nonlinear and based on outcomes with different distributions. Timeline Follow back (TLFB), the most widely utilized and accepted measure for determining drug use outcomes, collects reports of daily use (e.g. used marijuana, yes/no; joints per day) on multiple drugs over specified intervals. But TLFB data are not analyzed as collected (e.g. repeated binomial and Poisson variables). Rather, data are typically collapsed into summaries like total days of use (or abstinence) during a trial, or collapsed over smaller intervals (e.g. monthly sums of days used) in an attempt to create normally distributed variables. Such composite scores sacrifice information, lose efficiency, and are often not normal. Abundant previously collected longitudinal data on outcomes with different distributions like those from TLFB exist, but an inability to analyze them as such prevents satisfactorily addressing scientifically and clinically relevant questions such as: What is the temporal relationship between use of two or more drugs? What is the temporal relationship between change in drug use and change in co-occurring disorders? Do co- occurring psychiatric symptoms remit with reductions in drug use, or do reductions in psychiatric symptoms precede reductions in drug use? At what point does reduction in one occur relative to the other? This project's first two aims propose rigorous theoretical derivation and simulation to develop a multivariate nonlinear mixed model (MvNLMIXED) to simultaneously estimate and compare nonlinear trajectories of substance use and comorbidity outcomes with different distributions over time and between groups (Aim 1), and to evaluate inter- relationships among those jointly modeled trajectories by estimating their association (Aim 2a) and the order of, and time-lag among, change in one relative to the other (Aim 2b). Aims 3 and 4 will apply MvNLMIXED methods to answer important questions in two pharmacotherapy trials for co-occurring Attention-Deficit Hyperactivity Disorder (ADHD) in adolescents who also received cognitive behavioral therapy for drug use: a trial of atomoxetine and a multi-site trial o Osmotic-Release Methylphenidate in NIDA's Clinical Trials Network. These novel analyses are expected to identify important inter-relationships among use of different drugs with each other (Aim 3) and with ADHD (Aim 4), potentially informing how treatment may be operating. Beyond their importance for evaluating inter-relationships in these two trials and among drug use and comorbidity in general, these novel methods are widely applicable to evaluating temporal relationships among any jointly modeled longitudinal variables, benefiting new research and enhancing the scientific value of existing databases. User-friendly software for these methods will be made available on multiple platforms.