Analysis of Multiple Informant Data in Psychiatry

描述（由申请人提供）：我们将开发统计方法，以解决旨在评估基于服务或基于试验的样本中心理健康结果的治疗效果的研究中的几个关键问题。在我们过去的工作（MH 54693）中，我们开发了一种通用方法来分析来自各种心理健康研究的多个线人数据。这项研究导致了更有效的方法的发展，用于分析多个线人的结果或多个线人的预测在横向和纵向研究。这些方法也被扩展到处理部分观察到的线人报告和线人结果不相称的情况，即，产出-以不同的尺度衡量或代表一个以上的结构。在这项研究奋进中，合乎逻辑的下一步是开发更强大的治疗效果测试和改进的统计方法，以了解治疗对多种不相称的心理健康结果的原因。解决这个问题的两种最常见的方法是对复合结果进行单一测试或对每个组成结果进行单独测试。然而，这些方法是公正的，有效的，只有在罕见的情况下，完整的数据。我们建议开发和说明一个框架，用于联合测试多个不相称的结果。这将包括开发和评估适用于横截面和纵向研究的方法，允许不完整的观察。我们将比较这些方法与针对每个结局、复合终点和全局检验使用单独模型的方法。这些方法将被扩展到使用因果推理方法的观察设置。我们将把这些方法应用于抑郁症、精神分裂症和双相情感障碍患者的队列。这些疾病造成了巨大的社会、个人和经济成本，并且经常使用多种结果来评估治疗效果。我们将开发的方法将允许更精确地理解治疗的因果效应和更强大的治疗效果测试。虽然该应用程序侧重于治疗，但我们的方法广泛适用于涉及多种结果的其他设置，包括基于遗传的关联研究。重性抑郁症，双相情感障碍和精神分裂症产生显着的个人，社会和经济成本，有迫切需要改进的方法来评估预防和干预研究的治疗效果。该提案将开发统计方法，以改善对精神病临床试验和具有多种结果的观察性研究的分析。这些新颖和创新的方法将改善对治疗效果的评估，并将有助于改善人口的健康。

DESCRIPTION (provided by applicant): We will develop statistical methodology to address several key issues in studies designed to assess treatment effects on mental health outcomes in service-based or trial-based samples. In our past work (MH54693) we developed a general approach to the analysis of multiple informant data arising from a wide variety of mental health studies. This research led to the development of more efficient methods for the analysis of multiple informant outcomes or multiple informant predictors in both cross-sectional and longitudinal studies. These methods have also been extended to handle partially observed informant reports and cases where informant outcomes are non-commensurate, i.e., out- comes measured on different scales or representing more than one construct. A logical next step in this research endeavor is the development of more powerful tests of treatment effects and improved statistical methods for understanding the causes of treatments on multiple non-commensurate mental health outcomes. The two most common approaches to this problem are single testing of a composite outcome or separate testing of each constituent outcome. However these approaches are unbiased and efficient only in the rare situation of complete data. We propose to develop and illustrate a framework for joint testing of multiple non-commensurate outcomes. This will include developing and evaluating methods applicable to cross-sectional and longitudinal studies allowing for incomplete observations. We will compare these methods to approaches using separate models for each outcome, composite endpoints and global tests. These methods will be extended to observational settings using causal inference methodologies. We will apply these methods to cohorts of patients with depression, schizophrenia, and bipolar disorder. These diseases exert significant social, personal, and economic costs, and multiple outcomes are often used to assess treatment effectiveness. The methods that we will develop will permit a more precise understanding of the causal effects of treatments and more powerful tests of treatment effects. While this application focuses on treatments, our methods are broadly applicable to other settings involving multiple outcomes including genetic-based association studies. Major depression, bipolar disorder and schizophrenia exert significant personal, social, and economic costs, and there is a pressing need to improve methodology to assess treatment effects for prevention and intervention studies. This proposal will develop statistical methodology to improve the analysis of psychiatric clinical trials and observational studies with multiple outcomes. These novel and innovative methods will improve the assessment of treatment effects and will contribute to improving the health of the population.