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  • Nonparametric relative survival analysis with the R package relsurv [Elektronski vir]
    Pohar Perme, Maja ; Pavlič, Klemen
    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.
    Source: Journal of statistical software [Elektronski vir]. - ISSN 1548-7660 (Vol. 87, iss. 8, Nov. 2018, str. 1-27)
    Type of material - e-article
    Publish date - 2018
    Language - english
    COBISS.SI-ID - 34065113

    Link(s):

    https://www.jstatsoft.org/article/view/v087i08
    DOI

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