Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data.

Abstract

This study proposes a generalized time-varying effect model that can be used to characterize a discrete longitudinal covariate process and its time-varying effect on a later outcome that may be discrete. The proposed method can be applied to examine two important research questions for daily process data: measurement reactivity and predictive validity. We demonstrate these applications using health risk behavior data collected from alcoholic couples through an interactive voice response system. The statistical analysis results show that the effect of measurement reactivity may only be evident in the first week of interactive voice response assessment. Moreover, the level of urge to drink before measurement reactivity takes effect may be more predictive of a later depression outcome. Our simulation study shows that the performance of the proposed method improves with larger sample sizes, more time points, and smaller proportions of zeros in the binary longitudinal covariate.