Predicting dropout in intensive longitudinal data: Extending the joint model for autocorrelated data

Intensive longitudinal data from ecological momentary assessment (EMA) are widely used in clinical research but often suffer from dropout, leading to reduced statistical power, invalid results, and poor treatment outcomes. Predicting dropout could help with its prevention. While existing methods utilize baseline covariates, few studies account for the temporal dynamics of EMA data or identify the exact timing of dropout. Joint models (JM) enable simultaneous modeling of longitudinal processes and time-to-event data, offering dynamic predictions. However, conventional JMs assume limited measurement occasions and do not account for the autocorrelation inherent in EMA data. We extended the standard JM by incorporating an autoregressive submodel, capturing temporal dependencies in EMA measurements. We validated our approach through simulation studies, demonstrating good parameter recovery across different missingness mechanisms (missing completely at random, missing at random, missing not at random) and high dropout prediction accuracy. We applied the JM to an existing empirical EMA data set, using baseline (e.g., depression) and time-varying (affect, intermittent missingness) predictors of dropout. The extended JM outperformed a baseline-only survival model in predicting dropout. The sensitivity analysis of the missingness mechanism revealed that fixed-effect estimates remained stable across different missing data mechanisms, whereas random-effect estimates for autocorrelation were sensitive to these assumptions. By integrating autoregressive components, the extended JM accommodates temporal dependencies and dynamically updates predictions of dropout risk. This approach improves dropout prediction in EMA studies and highlights the importance of utilizing JMs for predicting clinically relevant outcomes while integrating EMA data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).