Estimating the loss in expectation of life due to cancer using flexible parametric survival models

Statistics in Medicine

Volumn 32, Issue 30, 2013, Pages 5286-5300

  Andersson, Therese M L ^a   Dickman, Paul W ^a   Eloranta, Sandra ^a   Lambe, Mats ^a,b   Lambert, Paul C ^a,c  

^a KAROLINSKA INSTITUTE   (Sweden)

^b UPPSALA UNIVERSITY HOSPITAL   (Sweden)

^c UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Cancer; Expectation of life; Flexible parametric survival model; Mean survival; Population based; Relative survival

Indexed keywords

ADULT; AGED; ARTICLE; BLADDER CANCER; BREAST CANCER; CANCER MORTALITY; CANCER SURVIVAL; COLON CANCER; FLEXIBLE PARAMETRIC SURVIVAL MODEL; HUMAN; LIFE EXPECTANCY; MAJOR CLINICAL STUDY; MEAN SURVIVAL; MELANOMA; NEOPLASM; RELATIVE SURVIVAL; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS; SURVIVAL RATE; SWEDEN;

CANCER; EXPECTATION OF LIFE; FLEXIBLE PARAMETRIC SURVIVAL MODEL; MEAN SURVIVAL; POPULATION-BASED; RELATIVE SURVIVAL;

AGED; AGED, 80 AND OVER; FEMALE; HUMANS; LIFE EXPECTANCY; MIDDLE AGED; NEOPLASMS; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS;

EID: 84897030468     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5943     Document Type: Article

References (37)

1. Viscomi S, Pastore G, Dama E, Zuccolo L, Pearce N, Merletti F, Magnani C. Life expectancy as an indicator of outcome in follow-up of population-based cancer registries: the example of childhood leukemia. Annals of Oncology 2006; 17(1): 167-171.
2. Brown ML, Lipscomb J, Snyder C. The burden of illness of cancer: economic cost and quality of life. Annual Review of Public Health 2001; 22: 91-113. DOI: 10.1146/annurev.publhealth.22.1.91. http://dx.doi.org/10.1146/annurev.publhealth.22.1.91.
3. Dickman PW, Adami HO. Interpreting trends in cancer patient survival. Journal of Internal Medicine 2006; 260: 103-17.
4. Talbck M, Dickman PW. Estimating expected survival probabilities for relative survival analysis-exploring the impact of including cancer patient mortality in the calculations. European Journal of Cancer November 2011; 47(17): 2626-2632. DOI: 10.1016/j.ejca.2011.08.010. http://dx.doi.org/10.1016/j.ejca.2011.08.010.
5. Hinchliffe SR, Dickman PW, Lambert PC. Adjusting for the proportion of cancer deaths in the general population when using relative survival: a sensitivity analysis. Cancer Epidemiology April 2012; 36(2):148-152. DOI: 10.1016/j.canep.2011.09.007. http://dx.doi.org/10.1016/j.canep.2011.09.007.
6. Hakama M, Hakulinen T. Estimating the expectation of life in cancer survival studies with incomplete follow-up information. Journal of Chronic Diseases September 1977; 30(9):585-597.
7. Messori A, Trippoli S. A new method for expressing survival and life expectancy in lifetime cost-effectiveness studies that evaluate cancer patients (review). Oncology Reports 1999; 6(5):1135-1141.
8. Hwang JS, Wang JD. Monte Carlo estimation of extrapolation of quality-adjusted survival for follow-up studies. Statistics in Medicine July 1999; 18(13):1627-1640.
9. Chu PC, Wang JD, Hwang JS, Chang YY. Estimation of life expectancy and the expected years of life lost in patients with major cancers: extrapolation of survival curves under high-censored rates. Value Health December 2008; 11(7):1102-1109. DOI: 10.1111/j.1524-4733.2008.00350.x. http://dx.doi.org/10.1111/j.1524-4733.2008.00350.X.
10. Horm JW, Sondik EJ. Person-years of life lost due to cancer in the United States, 1970 and 1984. American Journal of Public Health November 1989; 79(11):1490-1493.
11. Mettlin C. Trends in years of life lost to cancer: 1970-1985. CA-A Cancer Journal for Clinicians 1989; 39(1):33-39.
12. Burnet NG, Jefferies SJ, Benson RJ, Hunt DP, Treasure FP. Years of life lost (YLL) from cancer is an important measure of population burden - and should be considered when allocating research funds. British Journal of Cancer January 2005; 92(2):241-245. DOI: 10.1038/sj.bjc.6602321. http://dx.doi.org/10.1038/sj.bjc.6602321.
13. Royston P. Flexible parametric alternatives to the Cox model, and more. The Stata Journal 2001; 1: 1-28.
14. Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine August2002; 21(15):2175-2197. http://dx.doi.org/10.1002/sim.1203.
15. Nelson CP, Lambert PC, Squire IB, Jones DR. Flexible parametric models for relative survival, with application in coronary heart disease. Statistics in Medicine December2007; 26(30):5486-5498. DOI: 10.1002/sim.3064. http://dx.doi.org/10.1002/sim.3064.
16. Lambert PC, Royston P. Further development of flexible parametric models for survival analysis. Stata Journal June2009; 9(2):265-290. http://ideas.repec.org/a/tsj/stataj/v9y2009i2p265-290.html.
17. Durrleman S, Simon R. Flexible regression models with cubic splines. Statistics in Medicine May 1989; 8(5):551-561.
18. Andersson TML, Dickman PW, Eloranta S, Lambert PC. Estimating and modeling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Medical Research Methodology 2011; 11:96.
19. Stoer J, Bulirsch R. Introduction to Numerical Analysis, (3rd edn.) Springer: New York, 2002.
20. Barlow L, Westergren K, Holmberg L, Talbck M. The completeness of the Swedish cancer register: a sample survey for year 1998. Acta Oncology 2009; 48(1):27-33. DOI: 10.1080/02841860802247664. http://dx.doi.org/10.1080/02841860802247664.
21. Ederer F, Axtell LM, Cutler SJ. The relative survival rate: a statistical methodology. National Cancer Institute Monograph 1961; 6: 101-121.
22. Ederer F, Heise H. Instructions to IBM 650 programmers in processing survival computations, 1959. Methodological note No. 10, End Results Evaluation Section, National Cancer Institute, Bethesda MD.
23. Rutherford MJ, Dickman PW, Lambert PC. Comparison of methods for calculating relative survival in population-based studies. Cancer Epidemiology February2012; 36(1):16-21. DOI: 10.1016/j.canep.2011.05.010. http://dx.doi.org/10.1016/j.canep.2011.05.010.
24. Abdel-Rahman M, Stockton D, Rachet B, Hakulinen T, Coleman MP. What if cancer survival in Britain were the same as in Europe: how many deaths are avoidable? British Journal of Cancer December 2009; 101 Suppl 2: S115-S124. DOI: 10.1038/sj.bjc.6605401. http://dx.doi.org/10.1038/sj.bjc.6605401.
25. Lambert PC, Holmberg L, Sandin F, Bray F, Linklater KM, Purushotham A, Robinson D, Mller H. Quantifying differences in breast cancer survival between England and Norway. Cancer Epidemiology December 2011; 35(6):526-533. DOI: 10.1016/j.canep.2011.04.003. http://dx.doi.org/10.1016/j.canep.2011.04.003.
26. Brenner H, Gefeller O, Stegmaier C, Ziegler H. More up-to-date monitoring of long-term survival rates by cancer registries: an empirical example. Methods of Information in Medicine 2001; 40: 248-52.
27. Brenner H, Hakulinen T. Long-term cancer patient survival achieved by the end of the 20th century: most up-to-date estimates from the nationwide Finnish cancer registry. British Journal of Cancer 2001; 85: 367-371.
28. Beck JR, Kassirer JP, Pauker SG. A convenient approximation of life expectancy (the "DEALE"). I. validation of the method. American Journal of Medicine December1982; 73(6):883-888.
29. Gelber RD, Goldhirsch A, Cole BF. Parametric extrapolation of survival estimates with applications to quality of life evaluation of treatments. International breast cancer study group. Controlled Clinical Trials December1993; 14(6):485-499.
30. Caro JJ, Ishak KJ, Migliaccio-Walle K. Estimating survival for cost-effectiveness analyses: a case study in atherothrombosis. Value Health 2004; 7(5):627-635. DOI: 10.1111/j.1524-4733.2004.75013.x. http://dx.doi.org/10.1111/j.1524-4733.2004.75013.X.
31. Straatman H, Verbeek ALM, Peer PGM, Borm G. Estimating life expectancy and related probabilities in screen-detected breast cancer patients with restricted follow-up information. Statistics in Medicine February 2004; 23(3):431-448. DOI: 10.1002/sim.1539. http://dx.doi.org/10.1002/sim.1539.
32. Royston P, Parmar MKB. The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Statistics in Medicine August 2011; 30(19):2409-2421. DOI: 10.1002/sim.4274. http://dx.doi.org/10.1002/sim.4274.
33. Tappenden P, Chilcott J, Ward S, Eggington S, Hind D, Hummel S. Methodological issues in the economic analysis of cancer treatments. European Journal of Cancer November2006; 42(17):2867-2875. DOI: 10.1016/j.ejca.2006.08.010. http://dx.doi.org/10.1016/j.ejca.2006.08.010.
34. Nelson CL, Sun JL, Tsiatis AA, Mark DB. Empirical estimation of life expectancy from large clinical trials: use of left-truncated, right-censored survival analysis methodology. Statistics in Medicine November2008; 27(26):5525-5555. DOI: 10.1002/sim.3355. http://dx.doi.org/10.1002/sim.3355.
35. Demiris N, Sharples LD. Bayesian evidence synthesis to extrapolate survival estimates in cost-effectiveness studies. Statistics in Medicine June2006; 25(11):1960-1975. DOI: 10.1002/sim.2366. http://dx.doi.org/10.1002/sim.2366.
36. Demiris N, Lunn D, Sharples LD. Survival extrapolation using the poly-Weibull model. Statistical Methods in Medical Research November 2011, DOI: 10.1177/0962280211419645. http://dx.doi.org/10.1177/0962280211419645.
37. Smith PL. Splines as a useful and convenient statistical tool. The American Statistician 1979; 33: 57-62.