STATISTICAL METHODOLOGY FOR MENTAL HEALTH RESEARCH

心理健康研究的统计方法

• 批准号: 3376075
• 负责人: RODERICK J. LITTLE
• 金额: $ 23.17万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 1982
• 资助国家: 美国
• 起止时间: 1982-09-28 至 1992-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3376075
• 关键词: case history; computer simulation; data collection methodology /evaluation; mental health epidemiology; statistics /biometry

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.

现代心理健康研究通常会导致大量复杂的数据 套。 特别是，遵循个人的纵向研究 时间在每个时间点测量各种变量，产生数据 存在缺失值或 样本中的损耗。 调查数据以检测发生率和 抑郁症的患病率涉及冗长的问卷来衡量 精神障碍，经常通过忽略的方法进行分析 样本设计中的复杂性。 组装这些数据集所需的精力和费用是 相当大，因此使用最佳方法对它们进行分析似乎是谨慎的 可用的。 但是，分析通常仅限于相对基础 统计方法。 我们的目标是开发统计方法 有效，适当地分析心理健康数据，并 使其他研究人员可以访问这些方法。 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。 具有此结构的数据是 通常以效率低下和不适当的方法和状态来处理 艺术方法虽然改进，但通常是基于假设 这可能不适合心理健康结果。 统计 测试主要基于大样本理论，这是不合适的 对于通常在NIMH研究中收集的小型数据集。 我们 建议解决这两个问题，并进一步发展班级 NIMH数据有用的重复测量模型。 我们研究的第二个重点是对心理健康的分析 调查，重点是无响应调整。 异常值的问题 缺少数据通常是保留调查数据的重要原因 未完全分析。 我们建议开发缺失数据调整 这在幼稚的插补方法或方法上有了很大的改善 只是放弃了不完整的案件。 我们的方法将建立在 最近开发了多个插补的方法论， 单位无响应的加权调整技术。