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

Fuente: arXiv
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Hauptverfasser: Doretti, Marco, Stanghellini, Elena, Taraborrelli, Alessandro
Format: Preprint
Veröffentlicht: 2025
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author Doretti, Marco
Stanghellini, Elena
Taraborrelli, Alessandro
author_facet Doretti, Marco
Stanghellini, Elena
Taraborrelli, Alessandro
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_11776
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Handling outcome-dependent missingness with binary responses: A Heckman-like model
Doretti, Marco
Stanghellini, Elena
Taraborrelli, Alessandro
Methodology
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.
title Handling outcome-dependent missingness with binary responses: A Heckman-like model
topic Methodology
url https://arxiv.org/abs/2511.11776