Stratified Cox Model with Interactions in Analysis of Recurrent Events

Beata Bieszk‑Stolorz

doi:10.18778/0208-6018.335.14

Authors

Beata Bieszk‑Stolorz University of Szczecin, Faculty of Economics and Management, Institute of Econometrics and Statistics

DOI:

https://doi.org/10.18778/0208-6018.335.14

Keywords:

survival analysis, recurrent events, stratified Cox model, discontinuous risk intervals, unemployment

Abstract

The purpose of this paper is the assessment of relative intensity of exit from registered unemployment by means of the analysis of recurrent survival episodes and the comparison of these results with the results obtained for an individual episode. The stratified Cox model with interactions was used. Statistical data collected by labour offices indicate that a large fraction of the unemployed persons is registered multiple times. However, many of them resign from the mediation of labour offices and are subsequently removed from the register. In the article, the intensities of de‑registration due to various causes for men and women were compared. The study data came from the database of personal details of people registered by the Poviat Labour Office in Szczecin in 2013. The observation covered the records of their registration until the end of 2014. Gender of the unemployed persons influenced the intensity of de‑registrations in the first episodes, partially in the second and third ones, due to various causes, such as finding a job or removal from the register, whereas it did not influence the intensity of de‑registrations in the fourth and subsequent episodes. As for the other causes in the subsequent episodes, the differences were also not statistically significant. The proposed analysis may be important for implementing a good policy in the labour market. The identification of persons that resign from the mediation of the labour office is as interesting as the identification of these persons who find a job.

Downloads

Download data is not yet available.

References

Aalen O.O., Borgan O., Gjessing H.K. (2008), Survival and Event History Analysis. A Process Point of View, Springer, New York.
Google Scholar

Bieszk‑Stolorz B. (2017a), Cumulative Incidence Function in Studies on the Duration of the Unemployment Exit Process, “Folia Oeconomica Stetinensia”, vol. 17, issue 1, pp. 138–150.
Google Scholar

Bieszk‑Stolorz B. (2017b), Funkcja skumulowanej częstości i modele hazardu w ocenie konkurujących form wyjścia z bezrobocia, “Taksonomia”, no. 29, “Prace Naukowe UE we Wrocławiu”, no. 469, pp. 21–31, doi: 10.15611/pn.2017.469.02.
Google Scholar

Bieszk‑Stolorz B., Markowicz I. (2012), Modele regresji Coxa w analizie bezrobocia, CeDeWu, Warszawa.
Google Scholar

Bijwaard G.E., Franses P.H., Paap R. (2006), Modeling Purchases as Repeated Events, “Journal of Business & Economic Statistics”, vol. 24, no. 4, pp. 487–502, doi.org
Google Scholar

Cook R.J., Lawless J.F. (2007), The Statistical Analysis of Recurrent Events, Springer, New York.
Google Scholar

Gałecka‑Burdziak E. (2016), Multiple unemployment spells duration in Poland, Szkoła Główna Handlowa, Kolegium Analiz Ekonomicznych Working Papers Series, 2016/019, http://kolegia.sgh.waw.pl/pl/KAE/Documents/WorkingPapersKAE/WPKAE_2016_019.pdf [accessed: 15.01.2018].
Google Scholar

Gałecka‑Burdziak E., Góra M. (2017), How Do Unemployed Workers Behave Prior to Retirement? A Multi‑State Multiple‑Spell Approach, Discussion Paper Series, IZA DP no. 10680, ftp.iza.org [accessed: 15.01.2018].
Google Scholar

Guo Z., Gill T.M., Allore H.G. (2008), Modeling repeated time‑to‑event health conditions with discontinuous risk intervals: an example of a longitudinal study of functional disability among older persons, “Methods of Information in Medicine”, vol. 47, issue 2, pp. 107–116.
Google Scholar

Hosmer D.W., Lemeshow S. (1999), Applied Survival Analysis. Regression Modeling of Time to Event Data, John Wiley & Sons, New York.
Google Scholar

Jiang S.T, Landers T.L. Rhoads T.R. (2006), Proportional Intensity Models Robustness with Overhaul Intervals, “Quality and Reliability Engineering International”, vol. 22, issue 3, pp. 251–263.
Google Scholar

Kaplan E.L., Meier P. (1958), Non‑parametric estimation from incomplete observations, “Journal of American Statistical Association”, vol. 53, pp. 457–481.
Google Scholar

Kleinbaum D., Klein M. (2005), Survival Analysis. A Self‑Learning Text, Springer, New York.
Google Scholar

Machin D., Cheung Y.B., Parmar M.K.B. (2006), Survival Analysis. A Practical Approach. Second Edition, John Wiley & Sons, Chichester.
Google Scholar

Prentice R.L., Williams B.J., Peterson A.V. (1981), On the regression analysis of multivariate failure time data, “Biometrika”, no. 68, pp. 373–379.
Google Scholar

Sagara I., Giorgi R., Doumbo O.K., Piarroux R., Gaudart J. (2014), Modelling recurrent events: comparison of statistical models with continuous and discontinuous risk intervals on recurrent malaria episodes data, “Malaria Journal”, no. 13, pp. 293.
Google Scholar

Sączewska‑Piotrowska A. (2015), Badanie ubóstwa z zastosowaniem nieparametrycznej estymacji funkcji przeżycia dla zdarzeń powtarzających się, “Przegląd Statystyczny”, R. LXII, z. 1, pp. 29–51.
Google Scholar

Sokołowski A. (2010), Jak rozumieć i wykonywać analizę przeżycia, https://media.statsoft.pl/_old_dnn/downloads/jak_rozumiec_i_wykonac_analize_przezycia.pdf [accessed: 20.01.2018].
Google Scholar

Tan K.S. (2014), Regression Modeling of Longitudinal Outcomes With Outcome‑Dependent Observation Times, “Publicly Accessible Penn Dissertations”, vol. 1467, http://repository.upenn.edu/edissertations/1467 [accessed: 15.06.2017].
Google Scholar

Twisk J.W.R., Smidt N., Vente W. de (2005), Applied analysis of recurrent events: a practical overview, “Journal of Epidemiology and Community Health”, vol. 59, issue 8, pp. 706–710.
Google Scholar