Missing not at random intensive longitudinal data with dynamic structural equation models.

Abstract

Intensive longitudinal designs are increasingly popular for assessing moment-to-moment changes in mood, affect, and interpersonal or health behavior. Compliance in these studies is never perfect given the high frequency of data collection, so missing data are unavoidable. Nonetheless, there is relatively little existing research on missing data within dynamic structural equation models, a recently proposed framework for modeling intensive longitudinal data. The few studies that exist tend to focus on methods appropriate for data that are missing at random (MAR). However, missing not at random (MNAR) data are prevalent, particularly when the interest is a sensitive outcome related to mental health, substance use, or sexual behavior. As a motivating example, a study on people with binge eating disorder that has large amounts of missingness in a self-report item related to overeating is considered. Missingness may be high because participants felt shame reporting this behavior, which is a clear case of MNAR and for which methods like multiple imputation and full-information maximum likelihood are less effective. To improve handling of MNAR intensive longitudinal data, embedding a Diggle-Kenward-type MNAR model within a dynamic structural equation model is proposed. This approach is straightforward to apply in popular software like Mplus and only requires a few extra lines of code relative to models that assume MAR. Results from the proposed approach are contrasted with results from models that assume MAR, and a simulation study is conducted to study performance of the proposed model with continuous or binary outcomes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).