Imputation for Moderate Sized Mental Health Studies

中等规模心理健康研究的插补

基本信息

批准号：
6750743
负责人：
THOMAS BELIN
金额：
$ 25.4万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-09-18 至 2007-06-30
项目状态：
已结题

项目摘要

In mental health research, it is common for there to be numerous variables measured, many of which may have some missing values. When sample sizes are modest, existing strategies that address missing data using multivariate statistical models can easily have too many parameters for the available data to estimate. This R01 proposal-revises an earlier application, seeking support to evaluate a newly-developed factor-based multivariate statistical method for imputation of missing values, to extend the method to accommodate longitudinal factor-analysis models, to integrate software programs into a user-friendly Windows-based interface, and to snake the programs available to researchers over the world-wide web. Two applied problems motivate the present work, one from a suicide prevention study by Rotheram-Borus, Piacentini, Van Rossem, et al. (1996), where because of scattered missing values only 107 of 140 subjects for whom data were collected were available for model fitting, and another from an investigation of medication use and unmet need among children who live in foster care in Los Angeles County, where scattered missing values resulted in only 216 out of 302 subjects being available for model fitting (Zima, Bussing, Crecelius, Kaufman, Belin 1999). The proposed statistical methods offer the prospect of including all subjects in these analyses, of incorporating information from partially observed cases, and of accurately reflecting uncertainty. Ideas from factor analysis are central to the strategy.

在心理健康研究中，常见的是测量许多变量，其中许多变量可能缺少值。当样本量适中时，使用多元统计模型解决丢失数据的现有策略很容易具有太多参数，无法估算可用的数据。该R01提案概述了较早的应用程序，寻求支持以评估新开发的基于因子的多元统计方法，以扩展纵向因子 - 分析模型的方法，以将软件程序集成到基于用户友好的Windows的界面中，并索取在世界范围内的研究人员，以索取在世界范围内的研究人员。两个应用问题激发了当前的工作，其中一项来自Rotheram-Borus，Piacentini，Van Rossem等人的自杀预防研究。 (1996), where because of scattered missing values only 107 of 140 subjects for whom data were collected were available for model fitting, and another from an investigation of medication use and unmet need among children who live in foster care in Los Angeles County, where scattered missing values resulted in only 216 out of 302 subjects being available for model fitting (Zima, Bussing, Crecelius, Kaufman, Belin 1999).所提出的统计方法提供了将所有受试者在这些分析中纳入其中的前景，将部分观察到的情况纳入信息，并准确反映不确定性。因素分析的想法是该战略的核心。