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BMC Medical Research Methodology

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Open Access 01-12-2021 | Research

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

Authors: David Moriña, Pedro Puig, Albert Navarro

Published in: BMC Medical Research Methodology | Issue 1/2021

Abstract

Background

Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero).

Methods

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 (structural and non-structural).

Results

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. 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 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.

Conclusions

The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB).

Available only for authorised users

Johns G. Presenteeism in the workplace: a review and research agenda. J Organ Behav. 2010;31(4):519–42. https://doi.org/10.1002/job.630.CrossRef

Cameron AC, Trivedi PK. Regression analysis of count data. New York: Cambridge University Press; 1998. https://doi.org/10.1017/CBO9780511814365.CrossRef

Lambert D. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1. https://doi.org/10.2307/1269547.CrossRef

Abiodun G, Makinde O, Adeola A, Njabo K, Witbooi P, Djidjou-Demasse R, et al. A dynamical and zero-inflated negative binomial regression modelling of malaria incidence in Limpopo Province, South Africa. Int J Environ Res Public Health. 2019;16(11). https://doi.org/10.3390/IJERPH16112000.

Zhao H, Pan Y, Wang C, Guo Y, Yao N, Wang H, et al. The effects of metal exposures on Charlson comorbidity index using zero-inflated negative binomial regression model: NHANES 2011-2016. Biol Trace Elem Res. 2021;199(6):2104–11. https://doi.org/10.1007/S12011-020-02331-4.CrossRefPubMed

Perumean-Chaney SE, Morgan C, McDowall D, Aban I. Zero-inflated and overdispersed: what’s one to do? J Stat Comput. 2013;83(9):1671–83. https://doi.org/10.1080/00949655.2012.668550http://dx.doi.org.sire.ub.edu/10.1080/00949655.2012.668550.

Paulo Favero L, de Freitas Souza R, Belfiore P, Luiz Corrêa H, Haddad MF, Paulo L, et al. Count data regression analysis: concepts, overdispersion detection, zero-inflation identification, and applications with R. Pract Assess Res Eval. 2021;26. https://doi.org/10.7275/44nn-cj68.

Vuong QH. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica. 1989;57(2):307. https://doi.org/10.2307/1912557.CrossRef

Cameron AC, Trivedi PK. Regression-based tests for overdispersion in the Poisson model. J Econ. 1990;46(3):347–64. https://doi.org/10.1016/0304-4076(90)90014-K.CrossRef

10.

Desmarais BA, Harden JJ. Testing for zero inflation in count models: bias correction for the Vuong test. Stata J. 2013;13(4):810–35. https://doi.org/10.1177/1536867X1301300408https://doi-org.sire.ub.edu/10.1177/1536867X1301300408.

11.

Barbu A, Wu T, Wu YN. Learning mixtures of bernoulli templates by two-round EM with performance guarantee. Electron J Stat. 2015;8:3004–30. https://doi.org/10.1214/14-EJS981.1305.0319v6.CrossRef

12.

Diop A, Diop A, Dupuy JF. Simulation-based inference in a zero-inflated Bernoulli regression model. Commun Stats: Simul Comput. 2016;45(10):3597–614. https://doi.org/10.1080/03610918.2014.950743.CrossRef

13.

Sun Z, Rosen O, Sampson AR. Multivariate Bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Biometrics. 2007;63(3):901–9. https://doi.org/10.1111/j.1541-0420.2007.00762.x.CrossRefPubMedPubMedCentral

14.

R Core Team: R: A Language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria (2021). R Foundation for statistical computing. https://www.R-project.org/.

15.

Moriña Soler D, Puig P, Navarro A. bayesZIB: Bayesian zero-inflated Bernoulli regression model. In: R package version 0.0.2; 2021.

16.

Zeileis A, Kleiber C, Jackman S. Regression models for count data in R. J Stat Softw. 2008;27(8)1–25.

17.

Eurofound. EWCS 2015 – Methodology [Internet]. https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015/ewcs-2015-methodology. Accessed 10 Sep 2021.

18.

Eurostat. EU labour force survey - methodology [Internet]. http://ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey_-_methodology. Accessed 10 Sep 2021.

19.

Navarro A, Salas-Nicás S, Moncada S, Llorens C, Molinero-Ruiz E. Prevalence, associated factors and reasons for sickness presenteeism: a cross-sectional nationally representative study of salaried workers in Spain, 2016. BMJ Open. 2018;8(7):021212. https://doi.org/10.1136/bmjopen-2017-021212.CrossRef

20.

Vives A, Amable M, Ferrer M, Moncada S, Llorens C, Muntaner C, et al. Employment precariousness and poor mental health: evidence from Spain on a new social determinant of health. J Environ Public Health. 2013;2013:978656. https://doi.org/10.1155/2013/978656.CrossRefPubMedPubMedCentral

21.

Navarro A, Salas‐Nicás S, Llorens C, Moncada S, Molinero‐Ruíz E, Moriña D. Sickness presenteeism: are we sure about what we are studying? A research based on a literature review and an empirical illustration. Am J Ind Med. 2019;62(7). https://doi.org/10.1002/ajim.22982.

Title: Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health
Authors: David Moriña
Pedro Puig
Albert Navarro
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Medical Research Methodology / Issue 1/2021
Electronic ISSN: 1471-2288
DOI: https://doi.org/10.1186/s12874-021-01427-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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