Statistical Analysis in Longitudinal Mental Health Study

描述(由申请人提供)：通常很难或不可能对可能与纵向精神健康结果有关的治疗(例如，是否指定有代表性的受款人)或相关的风险因素(例如，社会经济状况)进行“随机化”。因此，观察性研究在心理健康(服务)研究的许多关键领域发挥着重要作用。然而，在同时存在时间依赖或纵向混淆的情况下，使用标准方法(例如，使用多元回归来调整基线或依赖时间的差异)的统计分析可能会有偏见。在这项建议中，我们开发了新的分析策略来分析纵向心理健康数据，扩展了Robins等人的边缘结构框架，以解决基线和最重要的纵向混杂协变量可能偏离所观察到的感兴趣的独立变量和结果之间的关系的情况。建议的方法主要是为观察结果研究而开发的，但它们也适用于使用实验设计的研究，例如，当缺失量因治疗组而异时，或当治疗不依从性发生时。此外，我们还对潜在类方法进行了扩展，使之能够描述多个纵向变量之间的相互作用模式，从而更好地理解心理健康研究中这些变量之间的动态关系。在这项提议中，我们想强调两个重要的概念化。首先，我们对纵向变量和并发时间变量进行了区分。前者可能只是间歇性的，并且可能会有误差地测量。只要有在多个时间点测量的主要感兴趣的变量或结果，则假定后者的值是已知的。其次，我们认识到，观察性研究中的因果关系断言必须依赖于“没有不可测量的混淆”这一不可检验的假设。因此，对混杂因素的调整只能解决已经衡量的因素。因此，我们认为结果“归因于”已定义的风险因素或治疗方法。我们只是断言，我们的发现与因果假设是一致的，而不是它们本身证明了因果关系。

DESCRIPTION (provided by applicant): it is often difficult or impossible, to "randomize" treatment (e.g. to assign a representative payee or not) or the risk factors of interest (e.g., socioeconomic status) that may be associated with longitudinal mental health outcome. Observational studies thus play an important role in many crucial areas of mental health (services) research. However, in the presence of concurrent time-dependent or longitudinal confounding, statistical analysis with standard approaches (e.g., using multiple regression to adjust for baseline or time-dependent differences) is likely to be biased. In this proposal, we develop new analytic strategies for the analysis of longitudinal mental health data, extending the marginal structural framework of Robins et al, to address situations in which baseline, and most importantly, longitudinal confounding covariates may bias the observed relationships between independent variables of interest and outcomes. The proposed methods are primarily developed for use in observational outcome studies, but they also have applications in studies that use experimental designs as for example, when the amount of missing data varies by treatment group, or when treatment noncompliance occurs. In addition, we propose an extension of the latent class approach to allow description of interacting patterns among multiple longitudinal variables so as to improve our understanding of the dynamic relationship among such variables in mental health research. We want to emphasize two important conceptualizations in this proposal. First, we make the distinction between longitudinal variables and concurrent time-dependent variables. The former may only be available intermittently and may be measured with error. The value of the latter is assumed to be known whenever variables of primary interest or outcomes measured at multiple time points are available. Second, we recognize that assertions of causality in observational study must rely on the untestable assumptions of "no unmeasured confounding". As a result, adjustments for confounding can only address factors that have been measured. Therefore, we state that outcomes are "attributable" to the defined risk factors or treatments. We assert only that our findings are consistent with causal hypotheses, and not that they demonstrate causality itself.