Estimating net survival: The importance of allowing for informative censoring

Statistics in Medicine

Volumn 31, Issue 8, 2012, Pages 775-786

  Danieli, Coraline ^a,b,c,d   Remontet, Laurent ^a,b,c,d   Bossard, Nadine ^a,b,c,d   Roche, Laurent ^a,b,c,d   Belot, Aurélien ^a,b,c,d,e  

^a HOSPICES CIVILS DE LYON   (France)

^b UNIVERSITÉ DE LYON   (France)

^c UNIVERSITÉ LYON 1   (France)

^d LABORATOIRE DE BIOMÉTRIE ET BIOLOGIE EVOLUTIVE   (France)

^e INSTITUT DE VEILLE SANITAIRE   (France)

Author keywords

Excess hazard model; Informative censoring; Net survival; Relative survival

Indexed keywords

ARTICLE; CANCER MORTALITY; CANCER REGISTRY; CANCER STATISTICS; CANCER SURVIVAL; CONTROLLED STUDY; COVARIANCE; FOLLOW UP; HAZARD RATIO; MATHEMATICAL MODEL; NONPARAMETRIC TEST; NORMAL DISTRIBUTION; PARAMETRIC TEST; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; SURVIVAL TIME; SYSTEMATIC ERROR; VALIDATION PROCESS;

CAUSE OF DEATH; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; SURVIVAL ANALYSIS;

EID: 84858279925     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4464     Document Type: Article

References (32)

1. Percy C, Stanek E, Gloeckler L. Accuracy of cancer death certificates and its effect on cancer mortality statistics. American Journal of Public Health 1981; 71(3):242-250.
2. Ashworth TG. Inadequacy of death certification: proposal for change. Journal of Clinical Pathology 1991; 44(4):265-268.
3. Aalen O, Borgan Ø, Gjessing H. Survival and event history analysis. Springer-Verlag: New York, 2008.
4. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. Wiley: New York, 2002.
5. Perme MP, Stare J, Esteve J. On estimation in relative survival. Biometrics 2011. DOI: 10.1111/j.1541-0420.2011.01640.x.
6. Buckley JD. Additive and multiplicative models for relative survival rates. Biometrics 1984; 40(1):51-62.
7. Hakulinen T, Tenkanen L. Regression analysis of relative survival rates. Applied Statistics 1987; 36:309-317.
8. Andersen PK, Vaeth M. Simple parametric and nonparametric models for excess and relative mortality. Biometrics 1989; 45(2):523-535.
9. Esteve J, Benhamou E, Croasdale M, Raymond L. Relative survival and the estimation of net survival: elements for further discussion. Statistics in Medicine 1990; 9(5):529-538.
10. Ederer F, Heise H. Instructions to ibm 650 programmers in processing survival computations, methodological note no. 10, end results evaluation section. Technical report, National Cancer Institute, Bethesda MD 1959.
11. Bolard P, Quantin C, Abrahamowicz M, Esteve J, Giorgi R, Chadha-Boreham H, Binquet C, Faivre J. Assessing time-by-covariate interactions in relative survival models using restrictive cubic spline functions. Journal of Cancer Epidemiology and Prevention 2002; 7(3):113-122.
12. Giorgi R, Abrahamowicz M, Quantin C, Bolard P, Esteve J, Gouvernet J, Faivre J. A relative survival regression model using B-spline functions to model non-proportional hazards. Statistics in Medicine 2003; 22(17):2767-2784.
13. Lambert PC, Smith LK, Jones DR, Botha JL. Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time-dependent effects. Statistics in Medicine 2005; 24(24):3871-3885.
14. Remontet L, Bossard N, Belot A, Esteve J. An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies. Statistics in Medicine 2007; 26(10):2214-2228.
15. Nelson CP, Lambert PC, Squire IB, Jones DR. Flexible parametric models for relative survival, with application in coronary heart disease. Statistics in Medicine 2007; 26(30):5486-5498.
16. Perme MP, Henderson R, Stare J. An approach to estimation in relative survival regression. Biostatistics 2009; 10(1):136-146.
17. Sasieni PD. Proportional excess hazards. Biometrika 1996; 83(1):127-141.
18. Ederer F, Axtell LM, Cutler SJ. The relative survival rate: a statistical methodology. National Cancer Institute Monograph 1961; 6:101-21.
19. Hakulinen T. Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics 1982; 38:933-942.
20. Robins JM, Rotnitzky A. 1992. Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS Epidemilology: Methodological Issues, Jewell NP, Dietz K, Farewell VT (eds), Birkhäuser: Boston, 297-331.
21. Bossard N, Velten M, Remontet L, Belot A, Maarouf N, Bouvier AM, Guizard AV, Tretarre B, Launoy G, Colonna M, Danzon A, Molinie F, Troussard X, Bourdon-Raverdy N, Carli PM, Jaffre A, Bessaguet C, Sauleau E, Schvartz C, Arveux P, Maynadie M, Grosclaude P, Esteve J, Faivre J. Survival of cancer patients in France: a population-based study from The Association of the French Cancer Registries (FRANCIM). European Journal of Cancer 2007; 43(1):149-160.
22. Le Teuff G, Abrahamowicz M, Bolard P, Quantin C. Comparison of Cox's and relative survival models when estimating the effects of prognostic factors on disease-specific mortality: a simulation study under proportional excess hazards. Statistics in Medicine 2005; 24(24):3887-3909.
23. Therneau TM, Grambsch PM. Modeling Survival Data: extending the Cox Model. Springer-Verlag: New York, 2000.
24. Ross SM. Simulation, Fourth Edition. Elsevier Academic Press: Amsterdam, 2006.
25. Collet D.Modelling Survival Data in Medical Research. Section 6.1, 2nd ed. Chapman & Hall/CRC: London, 2003.
26. Robins JM. Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers, Proceedings of the American Statistical Association - Biopharmaceutical, 1993.
27. Cortese G, Scheike TH. Dynamic regression hazards models for relative survival. Statistics in Medicine 2008; 27(18):3563-3584.
28. Stare J, Pohar M, Henderson R. Goodness of fit of relative survival models. Statistics in Medicine 2005; 24(24):3911-3925.
29. Kooperberg C, Stone CJ, Truong YK. Hazard Regression. Journal of the American Statistical Association 1995; 90(429):78-94.
30. Abrahamowicz M, MacKenzie TA. Joint estimation of time-dependent and non-linear effects of continuous covariates on survival. Statistics in Medicine 2007; 26(2):392-408.
31. Sauerbrei W, Royston P, Look M. A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biometrical Journal 2007; 49(3):453-473.
32. Sauerbrei W, Royston P. Multivariable Model Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Wiley: Chichester, 2008.