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

TL;DR

This paper extends the Heckman model to binary outcomes with outcome-dependent missingness, using logistic regression and a correction term to address selection bias in such settings.

Contribution

It introduces a Heckman-like model for binary responses with outcome-dependent missingness, employing a new correction term based on relative risks.

Findings

Effective bias correction in binary outcome missing data scenarios

Extension of Heckman model with logistic regression framework

Derivation of a correction term analogous to inverse Mills' ratio

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.