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

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

SUBMITTER: Ji L 

PROVIDER: S-EPMC10676222 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature