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Relative survival analysis in R.

Maja Pohar¹, Janez Stare

¹Department of Medical Informatics, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia. maja.pohar@mf.uni-lj.si

Computer Methods and Programs in Biomedicine

|March 3, 2006

Summary

Relative survival analysis helps estimate excess mortality in patient cohorts when cause-specific data is unavailable. Our R package, relsurv, offers flexible modeling for this important statistical technique.

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Area of Science:

Biostatistics
Epidemiology
Survival Analysis

Background:

Relative survival analysis compares cohort survival to expected population mortality.
It is crucial when cause-specific mortality data is inaccurate or missing.
Existing software for relative survival modeling is fragmented and difficult to use.

Purpose of the Study:

To introduce the 'relsurv' R package for relative survival regression modeling.
To provide a unified and flexible tool for applying various relative survival techniques.
To simplify the selection and implementation of relative survival models for researchers.

Main Methods:

Development of the 'relsurv' package in the R statistical programming language.
Implementation of several established relative survival regression models.
Focus on user-friendly data input and model fitting capabilities.

Main Results:

The 'relsurv' package offers a comprehensive suite of functions for relative survival analysis.
It enables flexible fitting of different regression models within a single software environment.
The package addresses the limitations of existing fragmented software solutions.

Conclusions:

The 'relsurv' R package facilitates easier and more flexible application of relative survival models.
It aims to improve the accessibility and usability of relative survival techniques in research.
This tool supports better estimation of excess mortality in disease cohorts.

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