IMPROVING METHODS FOR MISSING DATA & CAUSAL INFERENCE

改进缺失数据的方法

• 批准号: 7052542
• 负责人: JOSEPH L SCHAFER
• 金额: $ 20.42万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7052542
• 关键词: computer program /software; computer system design /evaluation; drug abuse prevention; drug abuse therapy; health science research; human data; information dissemination; longitudinal human study; method development; statistics /biometry

Nonresponse, causal inference and latent variables are closely related; from a statistical viewpoint, all three can be regarded as missing-data problems. This project is a continuation of the efforts by Dr. Schafer and his colleagues since 1996 to enhance the science of drug abuse prevention and treatment through improved methods for handling missing data. It also continues the work previously led by Dr. Collins to model stage-sequential development of substance use and related phenomena through improved methods of latent-transition analysis (LTA). To these we have added another area: developing new tools for inference about causal effects from observational studies and broken randomized experiments. This project has six Specific Aims. First, we will work to improve practices used to analyze incomplete data in prevention and treatment through application, education and software development. Second, we will investigate the properties of the new "doubly robust" regression methods for missing values in longitudinal research. Third, we will work to develop robust strategies for imputing missing values thought to depart from the usual assumption of missing at random (MAR). Fourth, we will develop techniques for analyzing incomplete data when some of the missing values are thought to be MAR but others are not. Fifth, we will continue to develop, implement and apply Bayesian methods for statistical inference in LTA. Sixth, we will develop procedures for imputing counterfactual outcomes for causal inference in the presence of confounding.

无反应、因果推断和潜变量密切相关；从统计学的角度来看，这三个问题都可以被视为缺失数据问题。该项目是 Schafer 博士及其同事自 1996 年以来努力的延续，旨在通过改进处理缺失数据的方法来加强药物滥用预防和治疗的科学性。它还延续了柯林斯博士之前领导的工作，通过改进的潜在转变分析 (LTA) 方法来模拟物质使用和相关现象的阶段性发展。除此之外，我们还添加了另一个领域：开发新工具，用于从观察性研究和破碎的随机实验中推断因果效应。该项目有六个具体目标。首先，我们将通过应用、教育和软件开发，努力改进预防和治疗中分析不完整数据的做法。其次，我们将 研究纵向研究中缺失值的新“双稳健”回归方法的特性。第三，我们将努力制定稳健的策略来估算缺失值，这些缺失值被认为偏离了通常的随机缺失 (MAR) 假设。第四，当某些缺失值被认为是 MAR 而其他值不是时，我们将开发分析不完整数据的技术。第五，我们将继续开发、实施和应用贝叶斯方法在 LTA 中进行统计推断。第六，我们将制定在存在混杂的情况下为因果推理推算反事实结果的程序。