IMPROVING METHODS FOR MISSING DATA & CAUSAL INFERENCE

无反应、因果推理和潜变量密切相关；从统计学的角度来看，这三个问题都可以被视为数据缺失问题。这个项目是Schafer博士和他的同事自1996年以来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科学的努力的延续。它还继续了以前由柯林斯博士领导的工作，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顺序发展。在此基础上，我们又增加了一个领域：开发新的工具，从观察性研究和破碎的随机实验中推断因果关系。这个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，改进用于分析预防和治疗中不完整数据的做法。第二，我们将

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.