STATISTICS FOR LONGITUDINAL STUDIES OF MH SERVICES

此应用程序寻求对统计人员团队的支持， 心理健康科学家合作开发和 对收集的多种结果的回归方法进行验证， 心理健康服务的纵向研究。具体 将开发的方法将满足持续健康的需要， 服务研究也将在基础 心理健康学科，如遗传学，临床试验和 流行病学这项研究的方法学进展将 使这些学科的心理健康科学家能够更多地 有效地调查精神障碍及其模式， 在乎三个具体目标是：1。开发、验证和 传播新的多元回归方法 在纵向研究中收集的测量数据。具体 要开发的方法是：观察到多种结果 多次生存（至事件发生时间） 结果;以及纵向和生存数据的组合。到 比较新方法和现有方法在心理健康方面的应用 卫生服务数据集，以评估其相对优势， 并传播我们的发现。3.发展立场- 单独的统计软件，用于新方法和现有方法的统一 生存和纵向数据分析，并整合这些数据， Splus软件，这是一个广泛使用的统计软件包， 连接到其他流行软件，包括SAS和SPSS。该公司- 研究人员将作为一个团队工作，将：分析不同的健康 使用现有方法的服务数据集;确定和传播 科学推论的常见方法论障碍;建议 改进统计方法;研究其理论 在计算机软件中实施新程序; 通过分析我们的数据比较新的和现有的方法 并将结果传播给心理健康 服务和统计界。新的方法可以 有助于我们理解精神障碍和 护理，并最终改善心理健康状况。

This application seeks support for a team of statisticians and mental health scientists to collaborate on the development and validation of regression methods for multiple outcomes collected in longitudinal studies of mental health services. The specific methods to be developed will address the needs of ongoing health services research but also will have application in the basic mental health disciplines such as genetics, clinical trials and epidemiology. The methodologic advances from this research will enable mental health scientists in these disciplines to more efficiently investigate mental disorders and their patterns of care. The three specific aims are:1. To develop, validate, and disseminate new regression methods for multiple outcome measurements collected in longitudinal studies. The specific methods to be developed are for: multiple outcomes observed repeatedly through time; multiple survival (time-to-event) outcomes; and combinations of longitudinal and survival data.2. To compare the application of new and existing methods to mental health services data sets to assess their relative advantages and disadvantages and to disseminate our findings. 3. To develop stand- alone statistical software for new and existing methods unifying survival and longitudinal data analyses and also to integrate this software in Splus, a widely available statistical package with bridges to other popular software including SAS and SPSS. The co- investigators will work as a team that will: analyze diverse health services data sets using existing methods; identify and disseminate common methodologic barriers to scientific inferences; propose improved statistical methods; investigate their theoretical properties; implement the new procedures in computer software; compare the new and existing methods through analyses of our data sets; and disseminate the results to both the mental health services and statistical communities. The new methodologies can contribute to our understanding of mental disorders and patterns of care and ultimately to the improvement of mental health status.