Multilevel interrupted time series allowing non-linear interruption effects

Music City Center 

We propose a new interrupted time series method for causal inference for multivariate time series data with an interruption. This method can incorporate multiple response streams with or without a control and estimate non-linear interruption effects across groups. We specify a latent time varying mean model as well as a multilevel interruption effect and generalized additive model post intervention, which behaves like a flexible structured random effect, allowing for nonlinear interruption effects.

We show through simulation that our model formulation a) has good coverage, b) effectively predicts the counterfactual trend, and c) effectively estimates the interruption effect across groups. In our first application, we use our modeling strategy absent a control time series by estimating the effect of the COVID-19 pandemic on hospital care utilization for acute myocardial infarction (AMI, or heart attacks) amongst Medicare beneficiaries in 2018 - 2021. Our application with a control concerns the effect of introduction of the prostate specific antigen test in 1986 on prostate cancer incidence using SEER data from 1975 - 2000, using colon and lung cancer in men as a control.

causal inference

time series

econometrics

health policy

semiparametric

bayesian 

Health Policy Statistics Section