Nonparametric Relative Survival Analysis with the R Package relsurv

Relative survival methods are crucial with data in which the cause of death information is either not given or inaccurate, but cause-specific information is nevertheless required. This methodology is standard in cancer registry data analysis and can also be found in other areas. The idea of relative...

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Main Authors: Maja Pohar Perme, Klemen Pavlic
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2018-11-01
Series:Journal of Statistical Software
Subjects:
r
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/2680
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spelling doaj-60ff5b6595dd4e3aa2d27f93dfd1fa6a2020-11-25T03:30:13ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602018-11-0187112710.18637/jss.v087.i081263Nonparametric Relative Survival Analysis with the R Package relsurvMaja Pohar PermeKlemen PavlicRelative survival methods are crucial with data in which the cause of death information is either not given or inaccurate, but cause-specific information is nevertheless required. This methodology is standard in cancer registry data analysis and can also be found in other areas. The idea of relative survival is to join the observed data with the general mortality population data and thus extract the information on the disease-specific hazard. While this idea is clear and easy to understand, the practical implementation of the estimators is rather complex since the population hazard for each individual depends on demographic variables and changes in time. A considerable advance in the methodology of this field has been observed in the past decade and while some methods represent only a modification of existing estimators, others require newly programmed functions. The package relsurv covers all the steps of the analysis, from importing the general population tables to estimating and plotting the results. The syntax mimics closely that of the classical survival packages like survival and cmprsk, thus enabling the users to directly use its functions without any further familiarization. In this paper we focus on the nonparametric relative survival analysis, and in particular, on the two key estimators for net survival and crude probability of death. Both estimators were first presented in our package and are still missing in many other software packages, a fact which greatly hampers their frequency of use. The paper offers guidelines for the actual use of the software by means of a detailed nonparametric analysis of the data describing the survival of patients with colon cancer. The data have been provided by the Cancer Registry of Slovenia.https://www.jstatsoft.org/index.php/jss/article/view/2680relative survival analysisnet survivalcrude probability of deathr
collection DOAJ
language English
format Article
sources DOAJ
author Maja Pohar Perme
Klemen Pavlic
spellingShingle Maja Pohar Perme
Klemen Pavlic
Nonparametric Relative Survival Analysis with the R Package relsurv
Journal of Statistical Software
relative survival analysis
net survival
crude probability of death
r
author_facet Maja Pohar Perme
Klemen Pavlic
author_sort Maja Pohar Perme
title Nonparametric Relative Survival Analysis with the R Package relsurv
title_short Nonparametric Relative Survival Analysis with the R Package relsurv
title_full Nonparametric Relative Survival Analysis with the R Package relsurv
title_fullStr Nonparametric Relative Survival Analysis with the R Package relsurv
title_full_unstemmed Nonparametric Relative Survival Analysis with the R Package relsurv
title_sort nonparametric relative survival analysis with the r package relsurv
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2018-11-01
description Relative survival methods are crucial with data in which the cause of death information is either not given or inaccurate, but cause-specific information is nevertheless required. This methodology is standard in cancer registry data analysis and can also be found in other areas. The idea of relative survival is to join the observed data with the general mortality population data and thus extract the information on the disease-specific hazard. While this idea is clear and easy to understand, the practical implementation of the estimators is rather complex since the population hazard for each individual depends on demographic variables and changes in time. A considerable advance in the methodology of this field has been observed in the past decade and while some methods represent only a modification of existing estimators, others require newly programmed functions. The package relsurv covers all the steps of the analysis, from importing the general population tables to estimating and plotting the results. The syntax mimics closely that of the classical survival packages like survival and cmprsk, thus enabling the users to directly use its functions without any further familiarization. In this paper we focus on the nonparametric relative survival analysis, and in particular, on the two key estimators for net survival and crude probability of death. Both estimators were first presented in our package and are still missing in many other software packages, a fact which greatly hampers their frequency of use. The paper offers guidelines for the actual use of the software by means of a detailed nonparametric analysis of the data describing the survival of patients with colon cancer. The data have been provided by the Cancer Registry of Slovenia.
topic relative survival analysis
net survival
crude probability of death
r
url https://www.jstatsoft.org/index.php/jss/article/view/2680
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