SURVSOFT-Software for nonparametric survival analysis

Computer Methods and Programs in Biomedicine

Volumn 96, Issue 1, 2009, Pages 63-71

  Geiss, Karla ^a   Meyer, Martin ^a   Radespiel Tröger, Martin ^a,b   Gefeller, Olaf ^b  

^a Population Based Cancer Registry Bavaria   (Germany)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Cancer registries; Period analysis; Relative survival; Software; Survival analysis

Indexed keywords

CANCER REGISTRIES; PERIOD ANALYSIS; RELATIVE SURVIVAL; SOFTWARE; SURVIVAL ANALYSIS;

GRAPHICAL USER INTERFACES; MACROS;

BIOINFORMATICS;

ARTICLE; CANCER REGISTRY; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER PROGRAM; DATA ANALYSIS; NONPARAMETRIC TEST; OUTCOME ASSESSMENT; SURVIVAL; SURVIVAL RATE; SURVIVAL TIME;

HUMANS; NEOPLASMS; REGISTRIES; SOFTWARE; STATISTICS, NONPARAMETRIC; SURVIVAL ANALYSIS; USER-COMPUTER INTERFACE;

EID: 67650694041     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2009.04.002     Document Type: Article

References (27)

1. Parkin D.M., and Hakulinen T. Analysis of survival. In: Jensen O.M., Parkin D.M., Maclennan R., Muir C.S., and Skeet R.G. (Eds). Cancer Registration: Principle and Methods (1991), International Agency for Research on Cancer, Lyon 159-176 (IARC Scientific Publications No. 95)
2. Lee E.T., and Wang J.W. Statistical Methods for Survival Data Analysis. 3rd ed. (2003), Wiley-Interscience, Hoboken, NJ
3. Klein J.P., and Moeschberger M.L. Survival Analysis: Techniques for Censored and Truncated Data (1997), Springer, New York
4. Brenner H., and Gefeller O. An alternative approach to monitoring cancer patient survival. Cancer 78 (1996) 2004-2010
5. SURV3: Windows Software for Relative Survival Analysis, Finnish Cancer Registry, Helsinki, Finland, available at: http://www.cancerregistry.fi/surv3.
6. H. Brenner, O. Gefeller, SAS period and periodh: Period Analysis of Survival Data, available at: http://www.imbe.med.uni-erlangen.de/issan/SAS/period/period.htm.
7. P. Dickman, Estimating and modelling relative survival in SAS and Stata, available at: http://www.pauldickman.com/rsmodel.
8. SEER*Stat statistical software, Surveillance Research Program, NCI, Bethesda, MD, USA, available at: http://seer.cancer.gov/seerstat.
9. K. Cronin, A. Mariotto, S. Scoppa, D. Green, L. Clegg, Differences between Brenner et al. and NCI Methods for Calculating Period Survival, Statistical Research and Applications Branch, NCI, Technical Report #2003-02-A.
10. Brenner H., and Gefeller O. Deriving more up-to-date estimates of long-term patient survival. Journal of Clinical Epidemiology 50 (1997) 211-216
11. Gondos A., Arndt V., Holleczek B., Stegmaier C., Ziegler H., and Brenner H. Cancer survival in Germany and the United States at the beginning of the 21st century: An up-to-date comparison by period analysis. International Journal of Cancer 121 (2007) 395-400
12. Houterman S., Janssen-Heijnen M.L.G., van de Poll-Franse L.V., Brenner H., and Coebergh J.W.W. Higher long-term cancer survival rates in southeastern Netherlands using up-to-date period analysis. Annals of Oncology 17 (2006) 709-712
13. Verdecchia A., Francisci S., Brenner H., Gatta G., Micheli A., Mangone L., and Kunkler I. The EUROCARE-4 Working Group, Recent cancer survival in Europe: a 2000-02 period analysis of EUROCARE-4 data. The Lancet Oncology 8 (2007) 784-796
14. Steliarova-Foucher E., Arndt V., Parkin D.M., Berrino F., and Brenner H. Timely disclosure of progress in childhood cancer survival by 'period' analysis in the Automated Childhood Cancer Information System. Annals of Oncology 18 (2007) 1554-1560
15. Ederer F., Axtell L.M., and Cutler S.J. The relative survival rate: a statistical methodology. National Cancer Institute Monographs 6 (1961) 101-121
16. Henson D.E., and Ries L.A. The relative survival rate. Cancer 76 (1995) 1687-1688
17. Ederer F., and Heise H. The effect of eliminating deaths from cancer on general population survival rates. Methodological Note 11 (1959), End Results Evaluation Section, National Cancer Institute
18. Hakulinen T. Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics 38 (1982) 933-942
19. Greenwood M. The errors of sampling of the survivorship table. Reports on Public Health and Medical Subjects vol.33 (1926), Her Majesty's Stationery Office, London
20. Federal Statistical Office of Germany, http://www.destatis.de.
21. Fan X. Using commonly available software for bootstrapping in both substantive and measurement analyses. Educational and Psychological Measurement 63 (2003) 24-50
22. Yu B., Tiwari R.C., Cronin K.A., McDonald C., and Feuer E.J. CANSURV: a Windows program for population-based cancer survival analysis. Computer Methods and Programs in Biomedicine 80 (2005) 195-203
23. Pohar M., and Stare J. Relative survival analysis in R. Computer Methods and Programs in Biomedicine 81 (2006) 272-278
24. Verdecchia A., Capocaccia R., Santaquilani M., and Hakulinen T. Methods of survival data analysis and presentation issues. In: Berrino F., Capocaccia R., Estève J., Gatta G., Hakulinen T., Micheli A., Sant M., and Verdecchia A. (Eds). Survival of Cancer Patients in Europe: the EUROCARE-2 Study (1999), International Agency for Research on Cancer, Lyon 41-45 (IARC Scientific Publications No. 151)
25. Corazziari I., Quinn M., and Capocaccia R. Standard cancer patient population for age standardising survival ratios. European Journal of Cancer 40 (2004) 2307-2316
26. Peto R., Pike M.C., Armitage P., Breslow N.E., Cox D.R., Howard S.V., Mantel N., McPherson K., Peto J., and Smith P.G. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. Analysis and examples. British Journal of Cancer 35 (1977) 1-39
27. Hakulinen T., Tenkanen L., Abeywickrama K., and Päivärinta L. Testing equality of relative survival patterns based on aggregated data. Biometrics 43 (1987) 313-325