STATISTICAL METHODOLOGY FOR MENTAL HEALTH RESEARCH

现代心理健康研究往往会产生大量复杂的数据 布景。特别是，跟踪个体的纵向研究 时间测量每个时间点的各种变量，产生数据 这通常会因为缺少值或 样本中的消耗量。调查数据，以检测发病率和 抑郁症的患病率需要用很长的问卷来衡量 精神障碍，而且经常被忽视的方法分析 样本设计中的复杂性。 组装这些数据集所需的工作量和费用如下 相当可观，所以使用最好的方法分析它们似乎是谨慎的 可用。然而，分析往往局限于相对基础的 统计方法。我们的目标是开发统计方法 对精神健康数据进行有效和适当的分析，并 让其他研究人员可以使用这些方法。 NIMH研究人员面临的最常见问题之一是分析 不平衡的重复测量数据。具有此结构的数据有 经常被低效和不适当的方法处理，以及 ART方法虽然是一种改进，但通常是基于假设的 这可能不适合心理健康结果。统计 测试在很大程度上基于大样本理论，这是不合适的 对于在NIMH研究中经常收集的小数据集。我们 建议解决这两个问题，并进一步发展班级 NIMH数据的有用重复测量模型。 我们研究的第二个重点是关于精神健康的分析 调查，重点是无答复调整。离群点问题 数据缺失往往是留下调查数据的一个重要原因 分析不完全。我们建议开发缺失数据调整 这大大改进了幼稚的归罪方法，或方法 这只是丢弃了未完成的案例。我们的方法将建立在 最近发展起来的多重归因的方法论，以及关于共同的 机组无响应的权重调整技术。

Modern mental health studies often result in large complicated data sets. In particular, longitudinal studies that follow individuals over time measure a variety of variables at each time point, yielding data that are often complicated by the presence of missing values or attrition from the sample. Survey data to detect the incidence and prevalence of depression involve lengthy questionnaires to measure mental disorders, and often are analyzed by methods that ignore complexities in the sample design. The effort and expense required to assemble these data sets is considerable, so it seems prudent to analyze them using the best methods available. However, often the analysis is confined to relatively basic statistical methods. Our objectives are to develop statistical methods for efficient and appropriate analysis of mental health data, and to make these methods accessible to other researchers. One of the most common problems facing NIMH researchers is the analysis of unbalanced repeated measures data. Data with this structure are often treated by inefficient and inappropriate methods, and state of the art methods, although an improvement, are usually based on assumptions that may not be appropriate for mental health outcomes. Statistical tests are largely based on large-sample theory, which is inappropriate for the small data sets that are often collected in NIMH studies. We propose to tackle these two problems, and to develop further the class of useful repeated measures models for NIMH data. A second focus of our research concerns the analysis of mental health surveys, with emphasis on nonresponse adjustments. Problems of outliers and missing data are often a significant reason why survey data are left incompletely analyzed. We propose to develop missing-data adjustments that improve considerably on naive methods of imputation, or methods that simply discard the incomplete cases. Our methods will build on the recently developed methodology of multiple imputation, and on the common technique of weighting adjustment for unit nonresponse.