METHODS FOR INCOMPLETE MENTAL HEALTH SERVICES DATA

不完整的精神卫生服务数据的方法

• 批准号: 2035712
• 负责人: THOMAS BELIN
• 金额: $ 9.57万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-05-15 至 2002-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2035712
• 关键词: human data; mathematical model; mental health epidemiology; method development; model design /development; statistics /biometry

This is a FIRST proposal for five years and $340,000 direct expenditures. The primary focus of this proposal is to examine a number of issues related to incomplete data in mental health services. These incomplete data can occur either due to the nature of the research design, or due to non-response and attrition. The proposed study will develop more readily available algorithms for dealing with common patterns of missing data. There are five projects proposed over the period. The first study will develop a method for handling incomplete data in longitudinal studies where data may be missing on several (rather than just one) repeated measures, as well as baseline measures. The longitudinal outcomes will be modeled jointly, under the assumption that the nonresponse/missing data problems are ignorable. Marko-chain Monte Carlo techniques such as Gibbs sampling will be employed. The specific alternative approaches to be examined are described on page 27. Using the Medical Outcome Study (MOS) as a case example, the new approach will be compared with other ways of dealing with missing data. Dr. Belin has experience using this approach in a study by Liu, Taylor and Berlin (1995), applied to AIDS. An initial analysis using the MOS data is included in Preliminary Studies (pages 21-23). The second study will examine the use of follow-up data to deal with missing data issues, allowing for both ignorable and non-ignorable non-response. This approach depends on being able to partition initial non-responders into some who do respond to a second attempt at data collection and those who do not. Then an assumption is made about the relationship between the response for responders and non-responders, an assumption that can be tested using the follow-up responders. The application will be to a survey of patients in a study of team-based management in the public sector. Dr. Belin has employed a related model in a study (Belin et al., 193) related to undercounts in the Census, where there was some follow-up on some initial non-respondents. The third study will examine multiple sources of data in making quality of care determinations, where some of the data are only available for a subset of the cases. In many applications, it is not possible to classify a case based on a gold-standard method, but the study has done some validation work that provides a more accurate assessment using the higher quality data. Using data from multiple sources, log linear models can be estimated on the subset which has data from all sources. Based on these estimated relationships, on could then use multiple imputations or other approaches to deal with the missing data for the subset of individuals with fewer data sources. The application will be to a study of quality of care for schizophrenia in medication management. Dr. Belin has used the same approach in a cancer screening study. The fourth study will examine model selection based on incomplete categorical data in highly dimensional contingency tables, and in combining regression models across multiply-imputed data sets. The fifth study will deal with issues related to skewed outcomes and count data with incomplete data, as well as relying on normal theory models. The alternatives to be considered include Poisson and negative binomial models. A similar study will be conducted using the Box-Cox transformation for continuous measures. Attached are two papers that explore these approaches in the latter case. Each of these five products has a research plan, including the development of the estimator and some form of evaluation, either with a mental health data base or a simulation. Dr. Belin will also continue as an investigator in the Biostatistics Core of the UCLA Research Center on Managed Care for Psychiatric Disorders, (Dr. Kenneth Wells, PI).

这是五年的第一个建议，直接$ 340,000 支出。 该提议的主要重点是检查 与精神卫生服务中不完整数据有关的问题数量。 这些不完整的数据可能是由于 研究设计，或由于无响应和流失。 提议 研究将开发出更容易获得的算法来处理 丢失数据的常见模式。 提出了五个项目 在此期间。 第一个研究将开发一种处理方法 在纵向研究中可能缺少数据的数据不完整 几个（而不是仅一个）重复的措施以及基线 措施。 纵向结果将共同建模 假设无响应/丢失的数据问题是 可忽视。 马克链蒙特卡洛技术，例如吉布斯 采样将被使用。 具体的替代方法 检查了第27页。使用医学结果研究 （MOS）为例，将将新方法与 处理丢失数据的其他方法。 贝林博士有经验 在Liu，Taylor和Berlin（1995）的一项研究中使用这种方法， 应用于艾滋病。 包括使用MOS数据的初始分析 在初步研究中（第21-23页）。 第二项研究将检查 使用后续数据来处理丢失的数据问题，从而 可忽视和不可忽视的无响应。 这种方法取决于 能够将初始非反应者分配给一些人 响应第二次尝试数据收集的尝试，以及那些没有的人。 然后是对 对响应者和非反应者的响应，这可以是 使用后续响应者测试。 该申请将是 在公开研究基于团队管理的研究中对患者的调查 部门。 贝林博士在一项研究中采用了相关模型（Belin ET Al。，193）与普查中的底部有关 对一些初始非回答的后续行动。 第三项研究将 检查多个数据来源以建立护理质量 确定，其中一些数据仅用于子集 案件。在许多应用程序中，不可能对案例进行分类 基于金标准方法，但该研究做了一些 验证工作，可提供更准确的评估 更高质量的数据。 使用来自多个来源的数据，日志线性 可以在具有来自所有来源的数据的子集上估算模型。 基于这些估计的关系，可以使用多个 用于处理丢失数据的候选方法或其他方法 数据源较少的个体子集。 该申请将是 研究药物精神分裂症的护理质量 管理。 贝林博士在癌症中使用了相同的方法 筛选研究。 第四项研究将检查基于模型选择的 在高度维情况下不完整的分类数据上 表格，以及在跨倍数的回归模型结合时 数据集。 第五项研究将处理与偏斜有关的问题 结果和计算数据不完整的数据，并依靠 普通理论模型。 要考虑的替代方案包括 泊松和负二项式模型。 类似的研究将是 使用Box-Cox转换进行连续措施进行。 附件是两篇论文，可以在后者中探索这些方法 案件。 这五种产品中的每一个都有一个研究计划，包括 开发估计器和某种形式的评估，要么 心理健康数据库或模拟。 贝林博士也将继续 作为UCLA研究生物统计学核心的研究者 精神疾病的托管护理中心（肯尼斯博士 威尔斯，pi）。