MISSING DATA METHODS FOR SUBSTANCE USE SURVEYS

物质使用调查的缺失数据方法

• 批准号: 5209773
• 负责人: JOSEPH L SCHAFER
• 金额: --
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/5209773
• 关键词: adolescence (12-20); 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.

数据缺失在药物使用调查中造成特殊问题。 率高 关键手术变量的缺失使得有必要开发 有原则的、统计上合理的数据分析方法。 新的，高度 有效的缺失数据技术已经出现在统计学中， 文献，包括多重插补和迭代算法 但这些还没有找到进入主流的方式 预防研究。 这项拟议工作的第一个目标是介绍 最新技术将缺失数据纳入预防领域 通过创建谢弗算法的用户友好的软件实现， 并说明其在对来自《公约》的数据进行实质性分析时的用途。 青少年酒精预防试验（AAPT）的第二个目标是扩大 现有的缺失数据技术，以解决调查数据的独特性 预防研究中经常出现的问题： 变量，其响应比例等于零， 非英雄值之间的连续分布;（B）交互， 预测变量和响应变量之间的关系 （c）不可替代的缺失机制，其中， 变量缺失的概率取决于缺失的变量，或者 （d）多层次结构， 哪些人在更大的单位内进行测试，如教室或 学校;以及（e）纵向结构，其中收集的数据用于 一群人在多个场合。 对数据的实质性分析 将使用这些新方法进行AAPT，以确认 以前的统计结论的有效性和扩展的科学 与药物使用开始和预防有关的知识。