Zero inflation in intensive longitudinal data: why is it important and how should we deal with it?

This study addresses the challenge of analyzing intensive longitudinal data (ILD) with zero-inflated autoregressive processes. ILD, characterized by intensive longitudinal measurements, often exhibit excessive zeros and temporal dependencies. Neglecting zero-inflation or mishandling it can lead to biased parameter estimates and inaccurate conclusions. To overcome this issue, we propose a novel Zero-Inflated Process Change multilevel AutoRegressive model (ZIP-CAR) model that incorporates zero-inflation using a Bayesian framework. We compare the performance of the proposed method with existing methods through a simulation study and demonstrate its advantages in accurately estimating parameters and improving statistical power. Additionally, we apply the ZIP-CAR model to a real intensive longitudinal dataset on problematic drinking behaviors, highlighting its effectiveness in capturing autoregressive and cross-lag effects while accounting for zero-inflation. The results underscore the importance of addressing zero-inflation in ILD analysis and provide practical recommendations for researchers. Our proposed model offers a valuable tool for analyzing ILD with zero-inflated autoregressive processes, facilitating a more comprehensive understanding of dynamic behavioral changes over time.