Handling outcome-dependent missingness with binary responses: A Heckman-like model

Marco Doretti, University of Florence

Co-authors: Elena Stanghellini, University of Perugia; Alessandro Taraborrelli, University of Perugia

Abstract: In regression models with missing outcomes, selection bias can arise when the missingness mechanism depends on the outcome itself. This proposal focuses on an extension of the Heckman model to a setting where the outcome is binary and both the selection process and the outcome are modeled through logistic regression. A correction term analogous to the inverse Mills’ ratio is derived based on relative risks. Under given assumptions, such a strategy provides an effective tool for bias correction in the presence of informative missingness.