Variable Selection in Multi-State Models of Correlated Data: An Application to COVID-19 Vaccination

Music City Center 

Multi-state models (MSM) are the primary analytical approach used to depict patient transitions among multiple clinical states in medical research. MSM are typically complex, with multiple transition paths and many parameters. This complexity introduces computational and numerical challenges in parameter estimation and scientific difficulties in model interpretation. Compounding these issues is the inherent within-subject correlation. For example, in care transitions among patients receiving COVID-19 vaccines, transition times among different states within the same subject tend to be correlated. Failing to accommodate these correlations may lead to inefficient estimation and questionable inference. We propose a method for variable selection in MSM with correlated data by reparameterizing the likelihood function and approximating the penalty term with a hyperbolic tangent function. We conducted a simulation study to evaluate the accuracy of this approach and applied the method to data from an observational study of transitions among people receiving COVID-19 vaccines, focusing on four health states: healthy, infection, emergency department or hospital admission, and death.

Multi-state Model

Variable Selection

Correlated data

COVID-19

EHR Data 

Section on Statistical Learning and Data Science