Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty

Recurrent health events often involve complex inter-relationships between longitudinal biomarkers and time-to-event outcomes, further complicated by sparse, irregular data collection and time-dependent correlations among events. Traditional statistical methods frequently struggle with these complexities, resulting in biased estimates and suboptimal modeling performance. To address these challenges, we propose the Functional Regression with AutoregressIve fraiLTY (FRAILTY) method, a novel framework designed to jointly model longitudinal measurements and recurrent events, accommodating both scalar and functional covariates while capturing time-dependent correlations among events. The FRAILTY method employs a two-step estimation procedure. First, functional principal component analysis through conditional expectation (PACE) is applied to extract key temporal features from sparse and irregular longitudinal data. Second, the obtained scores are incorporated into a dynamic recurrent frailty model with an autoregressive structure to account for within-subject correlations across recurrent events. Simulation studies demonstrated that the FRAILTY method outperformed existing methods, such as those relying on B-spline basis functions and Bayesian joint modeling, by achieving lower integrated mean squared errors, higher concordance indices, and greater statistical power in detecting functional parameters. Its practical utility was further validated through applications to two datasets: the Systolic Blood Pressure Intervention Trial study and the Multicenter Collaboration to Study Treatment Outcomes in Nephrolithiasis Evaluation cohort.