Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

• Published in: Frontiers in digital health Vol. 5; p. 1099517
• Main Authors: Ji, Linying, Li, Yanling, Potter, Lindsey N., Lam, Cho Y., Nahum-Shani, Inbal, Wetter, David W., Chow, Sy-Miin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 2023
• Subjects: ecological momentary assessments; multilevel Bayesian vector autoregressive model; multilevel data; multilevel multiple imputation; non-ignorable missing data; multilevel data; non-ignorable missing data; multilevel Bayesian vector autoregressive model; multilevel multiple imputation; ecological momentary assessments
• ISSN: 2673-253X; 2673-253X
• DOI: 10.3389/fdgth.2023.1099517

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals’ affective dynamics and urge.