Modeling Clinical and Functional Depression Outcomes

描述（由申请人提供）：拟定的研究开发了统计学 描述功能状态和临床的纵向模式的模型 症状这些分析使用的数据是作为8项已完成分析的一部分收集的。 抑郁症和治疗抑郁症在初级保健环境的研究。 这些单独的研究使用了重叠的抑郁症状测量方法， 功能状态，并评估患者长达两年， 治疗开始。拟议的研究将增加我们对 抑郁结局的时间进程和纵向关系 临床和功能结果之间的关系。这些分析还将提供 关于个体的纵向模式变化的重要信息 反应 目标1：开发描述纵向临床和功能的模型 结果：拟议工作的主要重点是参数化建模， 使用分层建模方法的纵向结果。 同时描述纵向功能和 临床结果和这些结果之间的关联将建立在模型上 单变量纵向结果。我们的等级结构 模型将基于数据中自然出现的聚类。的一级 模型描述了个体内部的纵向结果。Level II模型 描述了个体之间纵向模式的变异性， 问题研究多变量模型将纳入临床和 第二层模型中的功能结果。第三层模型描述了 研究中个体预期纵向模式的变异性， 捕获分析的元分析成分。模型开发将 需要仔细注意在所有级别的分布选择， 模型层次 目标2：得出信息丰富的模型摘要：将使用分层模型 回答关于抑郁症的临床意义的问题 治疗后。我们将估计预计稳定时间， 功能和临床结果以及抑郁症状的水平， 一旦稳定，功能受损。最后，我们将研究 临床和功能结局变化的同步性。 方法：我们将分析跨研究的原始数据。初步单变量 使用聚类数据方法建模将指导分层 模型最终的分层模型将使用基于模拟的 使用SAS-IML实现的方法。单变量建模的一个关键组成部分是 多变量模型将建立在这些单变量模型的基础上。人口平均数 将使用蒙特-卡罗积分计算估计值。

DESCRIPTION (provided by applicant): The proposed research develops statistical models that describe longitudinal patterns of functional status and clinical symptoms. Data used for these analyses were collected as part of 8 completed studies of depression and treatment for depression in a primary care setting. These individual studies used overlapping measures of depressive symptoms and functional status, and assessed patients for up to two years following treatment initiation. The proposed research will increase our understanding of the time course of depression outcomes and the longitudinal relationship between clinical and functional outcomes. These analyses will also provide important information about variation in individuals' longitudinal patterns of response. Aim 1: Develop models that describe longitudinal clinical and functional outcomes: The primary focus of the proposed work is parametric modeling of longitudinal outcomes using hierarchical modeling approaches. Multivariate models that simultaneously describe longitudinal functional and clinical outcomes and associations between these outcomes will build on models derived for univariate longitudinal outcomes. The structure of our hierarchical models will be based on naturally occurring clusters in the data. The Level I model describes longitudinal outcomes within individuals. The Level II model describes variability in longitudinal patterns across individuals, within studies. Multivariate models will incorporate correlation between clinical and functional outcomes within the Level II model. The Level III model describes variability in individuals' expected longitudinal patterns across studies, capturing the metaanalytic component of the analyses. Model development will require careful attention to choice of distributions across all levels of the model hierarchy. Aim 2: Derive informative model summaries: The hierarchical models will be used to answer clinically meaningful questions about the course of depression following treatment. We will estimate the expected time to stabilization of functional and clinical outcomes and the level of depressive symptoms and functional impairment once stabilization has occurred. Finally, we will examine synchrony of change in clinical and functional outcomes. Methods: We will analyze primary data across studies. Preliminary univariate modeling using clustered data methods will guide development of hierarchical models. Final hierarchical models will be estimated using simulation-based methods implemented using SAS-IML. A key component of univariate modeling is Multivariate models will build on these univariate models. Population-average estimates will be calculated using Monte-Carlo integration.