Literature DB >> 16510208

Relative survival analysis in R.

Maja Pohar1, Janez Stare.   

Abstract

Relative survival techniques are used to compare the survival experience in a study cohort with the one expected should they follow the background population mortality rates. The techniques are especially useful when the cause-specific death information is not accurate or not available since they provide a measure of excess mortality in a group of patients with a certain disease. There are several approaches to modeling relative survival, but there is no widely used statistical package that would incorporate the relevant techniques. The existing software was mostly written by the authors of different methods, in different computer languages and with different requirements for the data input, which makes it almost impossible for a user to choose between available models. We describe our R package relsurv that provides functions for easy and flexible fitting of several relative survival regression models.

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Year:  2006        PMID: 16510208     DOI: 10.1016/j.cmpb.2006.01.004

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  46 in total

1.  Female breast cancer in Gipuzkoa: prognostic factors and survival.

Authors:  N Larrañaga; C Sarasqueta; P Martínez-Camblor; M J Mitxelena; A Mendiola; I Martínez-Pueyo; M Basterretxea
Journal:  Clin Transl Oncol       Date:  2009-02       Impact factor: 3.405

2.  Nationwide implementation of laparoscopic surgery for colon cancer: short-term outcomes and long-term survival in a population-based cohort.

Authors:  Kjartan Stormark; Kjetil Søreide; Jon Arne Søreide; Jan Terje Kvaløy; Frank Pfeffer; Morten T Eriksen; Bjørn S Nedrebø; Hartwig Kørner
Journal:  Surg Endosc       Date:  2016-02-23       Impact factor: 4.584

3.  Risk of Death among HIV Co-Infected Multidrug Resistant Tuberculosis Patients, Compared To Mortality in the General Population of South Africa.

Authors:  Samuel Om Manda; Lieketseng J Masenyetse; Joey L Lancaster; Martie L van der Walt
Journal:  J AIDS Clin Res       Date:  2013-07-02

Review 4.  Survival analysis in hematologic malignancies: recommendations for clinicians.

Authors:  Julio Delgado; Arturo Pereira; Neus Villamor; Armando López-Guillermo; Ciril Rozman
Journal:  Haematologica       Date:  2014-09       Impact factor: 9.941

5.  On comparison of net survival curves.

Authors:  Klemen Pavlič; Maja Pohar Perme
Journal:  BMC Med Res Methodol       Date:  2017-05-02       Impact factor: 4.615

6.  A Population-Based Study of Incidence, Presentation, Management and Outcome of Primary Thromboembolic Ischemia in the Upper Extremity.

Authors:  Jørgen B Vennesland; Kjetil Søreide; Jan Terje Kvaløy; Andreas Reite; Morten Vetrhus
Journal:  World J Surg       Date:  2019-09       Impact factor: 3.352

7.  A class of transformation covariate regression models for estimating the excess hazard in relative survival analysis.

Authors:  Binbing Yu
Journal:  Am J Epidemiol       Date:  2013-03-13       Impact factor: 4.897

8.  Prognosis of long-term survival considering disease-specific death in patients with chronic myeloid leukemia.

Authors:  M Pfirrmann; M Baccarani; S Saussele; J Guilhot; F Cervantes; G Ossenkoppele; V S Hoffmann; F Castagnetti; J Hasford; R Hehlmann; B Simonsson
Journal:  Leukemia       Date:  2015-09-29       Impact factor: 11.528

9.  Do Patients Live Longer After THA and Is the Relative Survival Diagnosis-specific?

Authors:  Peter Cnudde; Ola Rolfson; A John Timperley; Anne Garland; Johan Kärrholm; Göran Garellick; Szilard Nemes
Journal:  Clin Orthop Relat Res       Date:  2018-06       Impact factor: 4.176

10.  Quantitative electrocardiographic measures and long-term mortality in exercise test patients with clinically normal resting electrocardiograms.

Authors:  Eiran Z Gorodeski; Hemant Ishwaran; Eugene H Blackstone; Michael S Lauer
Journal:  Am Heart J       Date:  2009-07       Impact factor: 4.749

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