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A Model Averaging Approach to Causal Inference in Substance Abuse Prevention Research

药物滥用预防研究中因果推理的模型平均方法

基本信息

批准号：
9293998
负责人：
Bing Han
金额：
$ 28.54万
依托单位：
RAND CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9293998
关键词：
Address Adolescent Adoption Belief Comparative Study Confounding Factors (Epidemiology)Consensus Data Development Dropout Drug abuse Drug usage Educational Curriculum Effectiveness Effectiveness of Interventions Equilibrium Ethnic group Evaluation Experimental Designs Individual Intervention Literature Methodology Methods Middle School Student Modeling National Institute of Drug Abuse Nature Outcome Persons Prevention Prevention Research Prevention program Protocols documentation Race Randomized Randomized Controlled Trials Research Research Personnel Research Priority Review Literature Schools Services Software Tools Statistical Methods Substance abuse problem Techniques Testing Variant alcohol and other drug alcohol prevention base causal model design inclusion criteria innovation marijuana prevention method development non-compliance novel novel strategies prevent prevention evaluation programs software development substance abuse prevention treatment group usability

项目摘要

PROJECT ABSTRACT A Model Averaging Approach to Causal Inference in Substance Abuse Prevention Research Many evaluations of school-based preventions for alcohol and other drugs (AOD) use are either observational by design or by implementation given noncompliance and dropouts. The observational nature of prevention studies is a major challenge to researchers trying to understand an intervention's effectiveness because of the serious threats of selection and confounding biases (i.e., individuals who receive more of the intervention are often very different from those who receive less). This application proposes a three-year R01 study to develop a novel causal inference approach using model averaging that will provide a more robust solution than current approaches to this major methodological problem in prevention research. The Rubin Causal Model (RCM) is a general framework for causal inference with studies in which randomization is not possible or is compromised by implementation difficulties. While classic statistical techniques can be severely biased when the distribution of confounding variables differ between treated and control individuals, the RCM can reduce such biases from effectiveness estimates. NIDA has made the continued development of methods under the RCM framework a high research priority. Currently, a major difficulty for practitioners is to choose among the numerous RCM available approaches. A preliminary review suggests that more than 40 distinct RCM approaches have been proposed. Further, numerical and empirical studies show that the conclusions across methods can be highly variable and that many distinct approaches have been recommended by different authors. Thus, the most recommendable RCM approach for a specific application is often uncertain. To address this challenge, we propose to develop a novel model averaging approach to causal inference. When there are many candidate estimators, the optimal model averaging estimator has been shown to offer the best statistical efficiency among all candidate estimators and eliminate sensitivity from model choice. Despite their key advantages, model averaging methods for causal effects have not been thoroughly investigated in the literature to tackle the issue of choosing an RCM approach. We propose to develop causal inference model averaging methodology and develop a software tool to implement the new method. We will evaluate practical advantages of the method in numerical studies and in an application study evaluating the effectiveness of CHOICE, a prominent school-based prevention for AOD use.

项目摘要