Analysis of Multiple Informant Data in Psychiatry

描述(由申请人提供)：我们将开发统计方法来解决研究中的几个关键问题，这些研究旨在评估基于服务或基于试验的样本的治疗对精神健康结果的影响。在我们过去的工作(MH54693)中，我们开发了一种通用的方法来分析来自各种精神健康研究的多个信息者数据。这项研究开发了更有效的方法来分析横断面和纵向研究中的多个告密者结果或多个告密者预测因素。这些方法还扩展到处理部分观察到的举报人报告和举报人结果不相称的情况，即以不同的比例尺衡量的结果或代表一个以上的结构。这项研究的下一步合乎逻辑的步骤是开发更强大的治疗效果测试，并改进统计方法，以了解治疗对多个非相称心理健康结果的原因。解决这一问题的两种最常见的方法是对复合结果进行单独测试或对每个组成结果进行单独测试。然而，这些方法只有在数据完整的罕见情况下才是公正和有效的。我们建议制定和说明多个非相称结果的联合测试框架。这将包括开发和评估适用于允许不完全观察的横断面和纵向研究的方法。我们将对这些方法与针对每个结果、复合端点和全局测试使用单独模型的方法进行比较。这些方法将扩展到使用因果推理方法的观察性环境。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中。这些疾病造成了巨大的社会、个人和经济成本，而且多项结果经常被用来评估治疗效果。我们将开发的方法将使我们能够更准确地了解治疗的因果关系，并对治疗效果进行更有力的测试。虽然这项应用侧重于治疗，但我们的方法广泛适用于其他涉及多种结果的环境，包括基于遗传的关联研究。严重的抑郁症、双相情感障碍和精神分裂症造成了巨大的个人、社会和经济代价，迫切需要改进方法学来评估预防和干预研究的治疗效果。这项提议将开发统计方法，以改进对具有多种结果的精神病学临床试验和观察性研究的分析。这些新颖和创新的方法将改进对治疗效果的评估，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.