Multilevel non-linear interrupted time series analysis
Abstract
Recent advances in interrupted time series analysis permit characterization of a typical non-linear interruption effect through use of generalized additive models. Concurrently, advances in latent time series modeling allow efficient Bayesian multilevel time series models. We propose to combine these concepts with a hierarchical model selection prior to characterize interruption effects with a multilevel structure, encouraging parsimony and partial pooling while incorporating meaningful variability in causal effects across subpopulations of interest, while allowing poststratification. These models are demonstrated with three applications: 1) the effect of the introduction of the prostate specific antigen test on prostate cancer diagnosis rates by race and age group, 2) the change in stroke or trans-ischemic attack hospitalization rates across Medicare beneficiaries by rurality in the months after the start of the COVID-19 pandemic, and 3) the effect of Medicaid expansion in Missouri on the proportion of inpatient hospitalizations discharged with Medicaid as a primary payer by key age groupings and sex.
Cite
@article{arxiv.2511.05725,
title = {Multilevel non-linear interrupted time series analysis},
author = {RJ Waken and Fengxian Wang and Sarah A. Eisenstein and Tim McBride and Kim Johnson and Karen Joynt-Maddox},
journal= {arXiv preprint arXiv:2511.05725},
year = {2025}
}