Analysis of zero inflated dichotomous variables from a Bayesian perspective: Application to occupational health

TL;DR

This paper introduces a Bayesian methodology for modeling zero inflated Bernoulli data, effectively distinguishing between structural zeros and non-structural zeros, with application to occupational health and sickness presenteeism.

Contribution

It presents a novel Bayesian approach to separate structural and non-structural zeros in zero inflated Bernoulli data, improving estimation accuracy.

Findings

Method performs well in simulation studies

R package implementation available for public use

Application to occupational health illustrates practical utility

Abstract

This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structurals and non-structurals). Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community.