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

Linying Ji, Yanling Li, Lindsey N. Potter, Cho Y. Lam, Inbal Nahum-Shani, David W. Wetter, Sy Miin Chow

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1099517
JournalFrontiers in Digital Health
Volume5
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Health Informatics
  • Computer Science Applications

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