Bayesian Inference for Sample Surveys in the Presence of High-Dimensional Auxiliary Information

• Activity Number: 340 - SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
• Type: Contributed
• Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Survey Research Methods Section
• Author(s): Yutao Liu* and Andrew Gelman and Qixuan Chen
• Companies: Columbia University and Columbia University and Columbia University
• Keywords: Sample Surveys; High-Dimensional Auxiliary Information; Bayesian Multilevel Modeling; Stan

Abstract

The National Drug Abuse Treatment System Survey (NDATSS) is a panel survey of substance abuse treatment programs in the United States. In 2013, the NDATSS conducted its first wave of a panel survey on residential non-opioid treatment programs (non-OTPs). A random sample of programs was selected from a sampling frame constructed using the 2010 National Survey of Substance Abuse Treatment Services (N-SSATS), the latest available annual census of all substance abuse treatment programs in the United States when the NDATSS was planned. From 2010 to 2013, the population of residential non-OTPs changed, with new programs opened and some old programs closed. To account for the change in the population as well as potential response bias, we propose a Bayesian multilevel model to improve the survey inference of population quantities in the NDATSS using the newly released 2013 N-SSATS data, which contains a rich profile of up-to-date information about all residential non-OTPs in the nation. We utilize machine learning methods to handle high-dimensional auxiliary variables in the 2013 N-SSATS data. Computation is implemented in the Bayesian inference engine Stan.