Statistical Models of NP Functioning for MH Services

MH 服务的 NP 功能统计模型

• 批准号: 6702213
• 负责人: Curtis M Tatsuoka
• 金额: $ 21.53万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-03 至 2005-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6702213
• 关键词: Statistical; Models; NP; Functioning; MH

DESCRIPTION (provided by applicant): New methods for statistical analysis of neuropsychological (NP) assessments for mental health services research are being proposed which can provide accurate and precise information about cognitive deficits. The potential benefits of extracting richer and more precise cognitive information from NP data sets are very significant: This analytic approach has the potential to (1) unveil specific relationships between NP deficits and disability; (2) enhance our ability to select treatment and rehabilitative methods that target specific cognitive deficiencies; (3) enhance our sensitivity to detect treatment responses, and to detect differences in cognitive processing between different clinical groups; and (4) save clinical assessment time and assessment costs through sequential testing. The proposed analytic approach includes sequential testing techniques that can drastically shorten the length of NP assessment testing. This project has potentially broad applicability. The techniques and models can be used in other clinical applications when it is of interest to measure cognitive functioning.

描述（由申请人提供）：正在提出用于精神卫生服务研究的神经心理学（NP）评估的统计分析新方法，该方法可以提供有关认知缺陷的准确和精确信息。从NP数据集中提取更丰富和更精确的认知信息的潜在好处是非常显著的：这种分析方法有可能（1）揭示NP缺陷和残疾之间的特定关系;（2）提高我们选择针对特定认知缺陷的治疗和康复方法的能力;（3）提高我们检测治疗反应的灵敏度，并检测不同临床群体之间认知过程的差异;以及（4）通过顺序测试节省临床评估时间和评估成本。建议的分析方法包括顺序测试技术，可以大大缩短NP评估测试的长度。该项目具有潜在的广泛适用性。当对测量认知功能感兴趣时，该技术和模型可以用于其他临床应用。