Comparing net survival estimators of cancer patients

Statistics in Medicine

Volumn 35, Issue 11, 2016, Pages 1866-1879

  Seppä, Karri ^a   Hakulinen, Timo ^a   Läärä, Esa ^b   Pitkäniemi, Janne ^a,c  

^a FINNISH CANCER REGISTRY   (Finland)

^b UNIVERSITY OF OULU   (Finland)

^c UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Cancer; Estimators; Excess mortality; Mean squared error; Net survival; Simulation study

Indexed keywords

AGE STANDARDISED EDERER II ESTIMATOR; ARTICLE; BREAST CANCER; CANCER PATIENT; CANCER REGISTRY; CONTROLLED STUDY; ERROR; FEMALE; FINITE MIXTURE MODEL; FOLLOW UP; HUMAN; LONG TERM SURVIVAL; LUNG CANCER; MAJOR CLINICAL STUDY; MALE; MEAN SQUARED ERROR; MORTALITY; NET SURVIVAL; POHAR PERME ESTIMATOR; PROSTATE CANCER; RECTUM CANCER; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STOMACH CANCER; SURVIVAL; SURVIVAL RATE; THYROID CANCER; UNSTANDARDISED EDERER II ESTIMATOR; ADOLESCENT; ADULT; AGE; AGED; COMPUTER SIMULATION; EPIDEMIOLOGY; FINLAND; MIDDLE AGED; NEOPLASM; REGISTER; SURVIVAL ANALYSIS; VERY ELDERLY;

ADOLESCENT; ADULT; AGE FACTORS; AGED; AGED, 80 AND OVER; COMPUTER SIMULATION; FEMALE; FINLAND; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; NEOPLASMS; REGISTRIES; SURVIVAL ANALYSIS;

EID: 84953776467     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6833     Document Type: Article

References (32)

1. Estève J, Benhamou E, Croasdale M, Raymond L. Relative survival and the estimation of net survival: elements for further discussion. Statistics in Medicine. 1990; 9: 529-538.
2. Andersen PK, Keiding N. Interpretability and importance of functionals in competing risks and multistate models. Statistics in Medicine. 2012; 31: 1074-1088.
3. Ederer F, Axtell LM, Cutler SJ. The relative survival rate: A statistical methodology. National Cancer Institute Monograph. 1961; 6: 101-121.
4. Ederer F, Heise H. Instructions to IBM 650 Programmers in Processing Survival Computations. Methodological Note No. 10, End Results Evaluation Section, National Cancer Institute, Bethesda, MD, 1959.
5. Hakulinen T. Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics. 1982; 38: 933-942.
6. Coleman MP, Quaresma M, Berrino F, Lutz JM, De Angelis R, Capocaccia R, Baili P, Rachet B, Gatta G, Hakulinen T, Micheli A, Sant M, Weir HK, Elwood JM, Tsukuma H, Koifman S, E Silva GA, Francisci S, Santaquilani M, Verdecchia A, Storm HH, Young JL, CONCORD Working Group. Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncology. 2008; 9: 730-756.
7. Sant M, Allemani C, Santaquilani M, Knijn A, Marchesi F, Capocaccia R, EUROCARE Working Group. EUROCARE-4. Survival of cancer patients diagnosed in 1995-1999. Results and commentary. European Journal of Cancer. 2009; 45: 931-991.
8. Roche L, Danieli C, Belot A, Grosclaude P, Bouvier AM, Velten M, Iwaz J, Remontet L, Bossard N. Cancer net survival on registry data: use of the new unbiased Pohar Perme estimator and magnitude of the bias with the classical methods. International Journal of Cancer. 2013; 132: 2359-2369.
9. Pohar Perme M, Stare J, Estève J. On estimation in relative survival. Biometrics. 2012; 68: 113-120.
10. Dickman PW, Lambert PC, Coviello E, Rutherford MJ. Estimating net survival in population-based cancer studies. International Journal of Cancer. 2013; 133: 519-521.
11. Lambert PC, Dickman PW, Rutherford MJ. Comparison of different approaches to estimating age standardized net survival. BMJ Medical Research Methodology. 2015; 15: 64.
12. Rebolj Kodre A. Pohar Perme M. Informative censoring in relative survival. Statistics in Medicine. 2013; 32: 4791-4802.
13. De Angelis R, Sant M, Coleman MP, Francisci S, Baili P, Pierannunzio D, Trama A, Visser O, Brenner H, Ardanaz E, Bielska-Lasota M, Engholm G, Nennecke A, Siesling S, Berrino F, Capocaccia R; EUROCARE-5 Working Group. Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE-5-a population-based study. Lancet Oncology. 2014; 15: 23-34.
14. Allemani C, Weir HK, Carreira H, Harewood R, Spika D, Wang XS, Bannon F, Ahn JV, Johnson CJ, Bonaventure A, Marcos-Gragera R, Stiller C, Azevedo e Silva G, Chen WQ, Ogunbiyi OJ, Rachet B, Soeberg MJ, You H, Matsuda T, Bielska-Lasota M, Storm H, Tucker TC, Coleman MP; CONCORD Working Group. Global surveillance of cancer survival 1995-2009: analysis of individual data for 25, 676, 887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 2015; 385: 977-1010.
15. Seppä K, Hakulinen T, Pokhrel A. Choosing the net survival method for cancer survival estimation. European Journal of Cancer. 2015; 51: 1123-1129.
16. Danieli C, Remontet L, Bossard N, Roche L, Belot A. Estimating net survival: the importance of allowing for informative censoring. Statistics in Medicine. 2012; 31: 775-786.
17. Brenner H, Gefeller O. An alternative approach to monitoring cancer patient survival. Cancer. 1996; 78: 2004-2010.
18. Brenner H, Gefeller O, Hakulinen T. Period analysis for "up-to-date" cancer survival data: Theory, empirical evaluation, computational realisation and applications. European Journal Cancer. 2004; 40: 326-335.
19. Dickman PW, Coviello E, Hills M. STATA computer program strs.ado, version 1.4.1 (18 Feb 2015). Available in: http://www.pauldickman.com/rsmodel/stata/_colon [Accessed on 17 April 2015], 2015.
20. Dickman PW, Coviello E. Estimating and modeling relative survival. The Stata Journal. 2015; 15: 186-215.
21. Chiang CL. Introduction to Stochastic Processes in Biostatistics. Wiley: New York, 1968.
22. Greenwood M. 1926. The Natural Duration of Cancer. In Reports on Public Health and Medical Subjects. His Majestys Stationery Office: London; 1-26.
23. Coviello E, Dickman PW, Seppä K, Pokhrel A. Estimating net survival using a life-table approach. The Stata Journal. 2015; 15: 173-185.
24. Pokhrel A, Hakulinen T. How to interpret the relative survival ratios of cancer patients. European Journal of Cancer. 2008; 44: 2661-2667.
25. Corazziari I, Quinn M, Capocaccia R. Standard cancer patient population for age standardising survival ratios. European Journal of Cancer. 2004; 40: 2307-2316.
26. De Angelis R, Francisci S, Baili P, Marchesi F, Roazzi P, Belot A, Crocetti E, Pury P, Knijn A, Coleman M, Capocaccia R; EUROCARE Working Group. The EUROCARE-4 database on cancer survival in Europe: data standardisation, quality control and methods of statistical analysis. European Journal of Cancer. 2009; 45: 909-930.
27. Lambert PC, Dickman PW, Weston CL, Thompson JR. Estimating the cure fraction in population-based cancer studies by using finite mixture models. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2010; 59: 35-55.
28. Durrleman S, Simon R. Flexible regression models with cubic splines. Statistics in Medicine. 1989; 8: 551-561.
29. Seppä K, Hakulinen T, Kim H-J, Läärä E. Cure fraction model with random effects for regional variation in cancer survival. Statistics in Medicine. 2010; 29: 2781-2793.
30. Pohar M, Stare J. Relative survival analysis in R. Computer Methods and Programs in Biomedicine. 2006; 81: 272-278.
31. R Development Core Team. R: A Language and Environment for Statistical Computing, version 3.2.2. R Foundation for Statistical Computing: Vienna, Austria, 2015. Available from: http://www.R-project.org[Accessed 21 October 2015].
32. Miettinen J, Rantanen M, Seppä K, Pitkäniemi J. popEpi: Functions for Epidemiological Analysis using Population Data. R package version 0.2.1. Available from: http://CRAN.R-project.org/package=popEpi [Accessed 21 October 2015].