Improving Methods for Dealing with Missing Data in Drug Use and Addiction Research: The Use of Later-Retrieval in Ecological Momentary Assessment - Project Summary/Abstract
The proposed K01 Mentored Research Scientist Development Award will prepare Manshu Yang, Ph.D. to
become an independent researcher in developing cutting-edge and practical statistical methodology to address
the timely issue of missing data in drug use and addiction (DUA) research. Dr. Yang is currently an Assistant
Professor of Quantitative Health Psychology at the University of Rhode Island. The outlined proposal builds
upon her training and research experience in statistics and psychology and will facilitate her path to become a
scholar dedicated to bridging state-of-the-art methodologies and substantive theory to advance knowledge of
DUA etiology and intervention strategies. DUA has a myriad of deleterious impacts and continues to raise
public health concerns in the US. In the past two decades, ecological momentary assessment (EMA) has been
increasingly used to help researchers understand the influence of psychosocial and contextual factors on
substance use in the real world and nearly in real time, so that more efficacious interventions can be
developed accordingly. However, along with the opportunity of using EMA comes a significant methodological
challenge: missing responses are inevitable, often substantial, and not properly handled in data analysis,
hence significantly increasing researchers’ risks of reaching incorrect conclusions and developing ineffective or
even unsafe interventions. Current methods cannot address all the unique methodological challenges in DUA
EMA studies due to their complex missing data patterns and untestable missing data assumptions. On the
other hand, EMA brings a unique opportunity to address these challenges by re-prompting participants shortly
after they missed an EMA survey to retrieve their data. Such later-retrieved data are readily available from
existing data (e.g., morning reports capturing missed DUA consequences in the prior day) or can be easily
added to an automated EMA system without altering EMA schedule. The outlined proposal includes a
comprehensive mentorship and didactic plan to support Dr. Yang’s career development and advance her
knowledge and skills in DUA etiology/intervention, EMA design, data management, and analysis, Bayesian
missing data analysis, and statistical programming. Specifically, the aims of the proposed research study are
(1) to use all available data (initially observed and later retrieved) to characterize missing data mechanisms in
DUA EMA, (2) to develop a novel Bayesian method for handling missing data and making valid inference on
DUA etiology, (3) to develop a Bayesian sensitivity analysis method to test the robustness of findings to
possible departures from missing data assumptions. The proposed study will investigate missing data issues
and develop analysis methods using empirical EMA datasets from three NIDA-funded projects and computer-
simulated data in a cost-effective way. Statistical methods developed from the study will greatly help
researchers elucidate the real-world mechanisms of DUA and develop tailored, effective interventions.
