<abstract xmlns="http://www.w3.org/1999/xhtml"><p> A basic assumption in proportional intensity models is the proportionality, that each covariate has a multiplicative effect on the intensity. The proportionality assumption is a strong assumption which is not always necessarily reasonable and thus needs to be checked. The survival analysis often employs graphic methods to study hazard proportionality. In this paper a geometrical method for determining the value of the hazard function on the basis of the continuous survival function was proposed. This method can be used to compare the intensity of the event for objects belonging to two subgroups of the analysed population. If we have graphs of survival function, then an analysis of the tangents at a specific time and their roots enables us to find the intensity and to study the relationship between them for different subgroups. This method can also be useful when studying the proportionality of hazard. It is a condition for the use of the Cox proportional hazards model. The above method was used to evaluate the effect of unemployment benefit and gender on unemployment and on the intensity of finding a job.</p></abstract>

A basic assumption in proportional intensity models is the proportionality, that each covariate has a multiplicative effect on the intensity. The proportionality assumption is a strong assumption which is not always necessarily reasonable and thus needs to be checked. The survival analysis often employs graphic methods to study hazard proportionality. In this paper a geometrical method for determining the value of the hazard function on the basis of the continuous survival function was proposed. This method can be used to compare the intensity of the event for objects belonging to two subgroups of the analysed population. If we have graphs of survival function, then an analysis of the tangents at a specific time and their roots enables us to find the intensity and to study the relationship between them for different subgroups. This method can also be useful when studying the proportionality of hazard. It is a condition for the use of the Cox proportional hazards model. The above method was used to evaluate the effect of unemployment benefit and gender on unemployment and on the intensity of finding a job.

Geometric Method of Determining Hazard for the Continuous Survival Function

University of Szczecin Department of Econometrics and Statistics Faculty of Economics and Management Mickiewicza 64, 71-101 Szczecin, Poland

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Geometric Method of Determining Hazard for the Continuous Survival Function"}

A basic assumption in proportional intensity models is the proportionality, that each covariate has a multiplicative effect on the intensity. The...

Geometric Method of Determining Hazard for the Continuous Survival Function

Data publikacji: 30 gru 2015

Zakres stron: 22 - 33

Otrzymano: 23 sty 2015

Przyjęty: 29 kwi 2015

DOI: https://doi.org/10.1515/foli-2015-0031

Słowa kluczowe
non-proportional hazard, continuous survival function, geometric method, unemployment

© University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Geometric Method of Determining Hazard for the Continuous Survival Function

Data publikacji: 30 gru 2015

Zakres stron: 22 - 33

Otrzymano: 23 sty 2015

Przyjęty: 29 kwi 2015

DOI: https://doi.org/10.1515/foli-2015-0031

Słowa kluczowenon-proportional hazard, continuous survival function, geometric method, unemployment

© University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
non-proportional hazard, continuous survival function, geometric method, unemployment