Flexible and Interpretable Modeling of Overlapping Exposure Risks in Self-Controlled Case Series Analysis

Abstract

The self-controlled case series (SCCS) method is frequently employed to explore the relationship between transient exposures and subsequent health events, utilizing data from individuals who have experienced the event of interest. Conventional spline-based SCCS models typically do not account for overlapping exposure periods and fail to accommodate complex interactive effects among multiple exposures. In this paper, we introduce a novel semiparametric SCCS method that employs a functional partial-linear single index (PLSI) link function, allowing for the estimation of overlapping exposure risks. Our approach offers greater interpretability and flexibility compared with existing methods by consolidating multiple exposures into a single index and modeling complex interactions through a nonparametric link function. We validate our model through simulation studies comparing its performance with standard methods under various practical exposure settings. Furthermore, we apply our method to two real-world datasets involving MMR vaccination and malaria chemoprevention, demonstrating its practical utility and enhanced capability to handle multiple, overlapping exposures effectively. Our findings suggest that the PLSI-SCCS model is a robust tool for modern epidemiological and pharmaceutical research, providing a nuanced understanding of exposure effects, particularly in complex multi-exposure scenarios.