Comparison of different approaches to estimating age standardized net survival

BMC Medical Research Methodology

Volumn 15, Issue 1, 2015, Pages

  Lambert, Paul C ^a,b   Dickman, Paul W ^b   Rutherford, Mark J ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b KAROLINSKA INSTITUTE   (Sweden)

Author keywords

Epidemiology; Net survival; Relative survival

Indexed keywords

ADOLESCENT; ADULT; AGE; AGE DISTRIBUTION; AGED; ALGORITHM; CHILD; COMPARATIVE STUDY; COMPUTER SIMULATION; HUMAN; INFANT; MIDDLE AGED; MORTALITY; NEOPLASM; NEWBORN; PRESCHOOL CHILD; REGISTER; STATISTICS AND NUMERICAL DATA; SURVIVAL ANALYSIS; SURVIVAL RATE; THEORETICAL MODEL; VERY ELDERLY;

ADOLESCENT; ADULT; AGE DISTRIBUTION; AGE FACTORS; AGED; AGED, 80 AND OVER; ALGORITHMS; CHILD; CHILD, PRESCHOOL; COMPUTER SIMULATION; HUMANS; INFANT; INFANT, NEWBORN; MIDDLE AGED; MODELS, THEORETICAL; NEOPLASMS; REGISTRIES; SURVIVAL ANALYSIS; SURVIVAL RATE;

EID: 84939163774     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/s12874-015-0057-3     Document Type: Article

References (31)

1. Seppä K, Hakulinen T, Läärä E. Avoidable deaths and random variation in patients' survival. Br J Cancer. 2012; 106(11):1846-9. [doi: 10.1038/bjc.2012.169 ].
2. Ellis L, Coleman MP, Rachet B. How many deaths would be avoidable if socioeconomic inequalities in cancer survival in england were eliminated? a national population-based study, 1996-2006. Eur J Cancer. 2012; 48(2):270-8. doi: 10.1016/j.ejca.2011.10.008.
3. Derolf ÅR, Kristinsson SY, Andersson TM-L, Landgren O, Dickman PW, Bjorkholm M. Improved patient survival for acute myeloid leukemia: a population-based study of 9729 patients diagnosed in Sweden between 1973 and 2005. Blood. 2009; 113(16):3666-72. doi: 10.1182/blood-2008-09-179341.
4. Sant M, Allemani C, Santaquilani M, Knijn A, Marchesi F, Capocaccia R, et al. EUROCARE-4. survival of cancer patients diagnosed in 1995-1999: Results and commentary. Eur J Cancer. 2009; 45(6):931-91. doi: 10.1016/j.ejca.2008.11.018.
5. Coleman MP, Quaresma M, Berrino F, Lutz JM, Angelis RD, Capocaccia R, et al. Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol. 2008; 9(8):730-56.
6. Coleman MP, Forman D, Bryant H, Butler J, Rachet B, Maringe C, et al. Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, 1995-2007 (the international cancer benchmarking partnership): an analysis of population-based cancer registry data. Lancet. 2011; 377:127-38.
7. Cronin KA, Feuer EJ. Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival. Stat Med. 2000; 19(13):1729-1740.
8. Corazziari I, Quinn M, Capocaccia R. Standard cancer patient population for age standardising survival ratios. Eur J Cancer. 2004; 40(15):2307-316. doi: 10.1016/j.ejca.2004.07.002.
9. Moller H, Sandin F, Bray F, Klint A, Linklater KM, Purushotham A, et al. Breast cancer survival in England, Norway and Sweden: a population-based comparison. Int J Cancer. 2010; 127:2630-8.
10. Lambert PC, Holmberg L, Sandin F, Bray F, Linklater KM, Purushotham A, et al. Quantifying differences in breast cancer survival between England and Norway. Cancer Epidemiol. 2011; 35:526-33. doi: 10.1016/j.canep.2011.04.003.
11. Morris EJA, Sandin F, Lambert PC, Bray F, Klint Å, Linklater K, et al. A population-based comparison of the survival of patients with colorectal cancer in England, Norway and Sweden between 1996 and 2004. Gut. 2011; 60(8):1087-1093. doi: 10.1136/gut.2010.229575.
12. Holmberg L, Robinson D, Sandin F, Bray F, Linklater KM, Klint A, et al. A comparison of prostate cancer survival in England, Norway and Sweden: a population-based study. Cancer Epidemiol. 2012; 36(1):7-12. doi: 10.1016/j.canep.2011.08.001.
13. Holmberg L, Sandin F, Bray F, Richards M, Spicer J, Lambe M, et al. National comparisons of lung cancer survival in England, Norway and Sweden 2001-2004: differences occur early in follow-up. Thorax. 2010; 65(5):436-41. doi: 10.1136/thx.2009.124222.
14. Pohar Perme M, Stare J, Estève J. On estimation in relative survival. Biometrics. 2012; 68:113-20. doi: 10.1111/j.1541-0420.2011.01640.x.
15. Danieli C, Remontet L, Bossard N, Roche L, Belot A. Estimating net survival: the importance of allowing for informative censoring. Stat Med. 2012; 31(8):775-86. doi: 10.1002/sim.4464.
16. Dickman PW, Sloggett A, Hills M, Hakulinen T. Regression models for relative survival. Stat Med. 2004; 23(1):51-64. doi: 10.1002/sim.1597.
17. Lambert PC, Dickman PW, Nelson CP, Royston P. Estimating the crude probability of death due to cancer and other causes using relative survival models. Stat Med. 2010; 29:885-95.
18. Ederer F, Heise H. Instructions to IBM 650 programmers in processing survival computations. methodological note no. 10, end results evaluation section. Bethesda, MD: Technical report, National Cancer Institute; 1959.
19. Hakulinen T, Seppä K, Lambert PC. Choosing the relative survival method for cancer survival estimation. Eur J Cancer. 2011; 47(14):2202-210. doi: 10.1016/j.ejca.2011.03.011.
20. Dickman PW, Lambert PC, Coviello E, Rutherford MJ. Estimating net survival in population-based cancer studies. Int J Cancer. 2013; 133(2):519-21. doi: 10.1002/ijc.28041.
21. Seppä Ki, Hakulinen T, Pokhrel A. Choosing the net survival method for cancer survival estimation. Eur J Cancer. 2013. doi: 10.1016/j.ejca.2013.09.019.
22. Brenner H, Arndt V, Gefeller O, Hakulinen T. An alternative approach to age adjustment of cancer survival rates. Eur J Cancer. 2004; 40(15):2317-22. doi: 10.1016/j.ejca.2004.07.007.
23. Nelson CP, Lambert PC, Squire IB, Jones DR. Flexible parametric models for relative survival, with application in coronary heart disease. Stat Med. 2007; 26(30):5486-8. doi: 10.1002/sim.3064.
24. Rutherford MJ, Crowther MJ, Lambert PC. The use of restricted cubic splines to approximate complex hazard functions in the analysis of time-to-event data: a simulation study. J Stat Comput Simul. 2015; 85:777-93.
25. Burton A, Altman DG, Royston P, Holder RL. The design of simulation studies in medical statistics. Stat Med. 2006; 25(24):4279-92. doi: 10.1002/sim.2673.
26. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006; 25(1):127-41. doi: 10.1002/sim.2331.
27. Roche L, Danieli C, Belot A, Grosclaude P, Bouvier AM, Velten M, et al. Cancer net survival on registry data: Use of the new unbiased Pohar-Perme estimator and magnitude of the bias with the classical methods. Int J Cancer. 2012; 132(10):2359-69. doi: 10.1002/ijc.27830.
28. Hakulinen T. Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics. 1982; 38(4):933-42.
29. Rebolj Kodre A, Pohar Perme M. Informative censoring in relative survival. Stat Med. 2013; 32:4791-4802. doi: 10.1002/sim.5877.
30. Crowther MJ, Lambert PC. Simulating biologically plausible complex survival data. Stat Med. 2013; 32:4118-34. 10.1002/sim.5823.
31. Rutherford MJ, Dickman PW, Lambert PC. Comparison of methods for calculating relative survival in population-based studies. Cancer Epidemiol. 2012; 36(1):16-21. doi: 10.1016/j.canep.2011.05.010.