MISSING DATA METHODS FOR SUBSTANCE USE SURVEYS

物质使用调查的缺失数据方法

• 批准号: 6270044
• 负责人: JOSEPH L SCHAFER
• 金额: $ 16.16万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6270044
• 关键词: adolescence (12-20); behavioral /social science research tag; clinical research; computer data analysis; computer program /software; computer system design /evaluation; disease /disorder proneness /risk; drug abuse information system; drug abuse prevention; human data; longitudinal human study; method development; population survey; statistics /biometry; substance abuse related disorder; young adult human (21-34)

Missing data pose special problems in surveys of substance use. High rates of missingness on key surgery variables make it necessary to develop principled, statistically sound approaches for data analysis. New, highly effective missing-data techniques have appeared in the statistical literature, including multiple imputation and algorithms for iterative simulation but these have not yet found their way into mainstream prevention research. The first goal of this proposed work is to introduce state-of-the-art technology for missing data into athe field of prevention by creating user-friendly software implementations of Schafer's algorithms, and to demonstrate their use in substantive analyses of data from the Adolescent Alcohol Prevention Trial (AAPT) A second goal is to extend the available missing-data technology to address unique feature of survey data that frequently arise in prevention research: (a) semicontinuous variables, which have a proportion of responses equal to zero and a continuous distribution among the nonhero values; (b) interactions, in which the relationship between predictor and response variables varies among subgroups; (c) nonignorable missingness mechanisms, in which the probability that variables are missing depends on the missing variables, or on other unrecorded variables related to them; (d) multilevel structure, in which individuals are tested within larger units such as classrooms or schools; and (e) longitudinal structure, in which data are collected for a group of individuals on multiple occasions. Substantive analyses of data from the AAPT will be conducted using these new methods, to confirm the validity of previous statistical conclusions and extend scientific knowledge relating to substance-use onset and prevention.

在物质使用情况调查中，数据缺失带来了特殊的问题。高利率 关键手术变量的缺失使其有必要开发 原则性的、统计上可靠的数据分析方法。新的，高度的 有效的缺失数据技术已经出现在统计中 文献，包括多重推算和迭代算法 模拟，但这些还没有进入主流 预防研究。这项拟议工作的第一个目标是引入 将最先进的数据丢失技术带入预防领域 通过创建用户友好的Schafer算法的软件实现， 并演示它们在对来自 青少年酒精预防试验(AAPT)的第二个目标是延长 可用的缺失数据技术可解决测量数据的独特特征 在预防研究中经常出现的情况：(A)半连续 变量，这些变量的响应比例等于零，并且 在非英雄价值观中持续分布；(B)互动，在 预测变量和响应变量之间的关系是不同的 (C)不可忽视的失配机制，其中 变量丢失的概率取决于丢失的变量，或者 与之相关的其他未记录变量；(D)多级结构， 哪些人在更大的单位内进行测试，如教室或 学校；和(E)纵向结构，在这种结构中收集数据 一群人在多个场合。数据的实质性分析 将使用这些新方法进行AAPT，以确认 以往统计结论的有效性和科学性的延伸 与药物使用开始和预防有关的知识。