Breast Cancer Research and Treatment

, Volume 134, Issue 2, pp 839–851 | Cite as

Incidence of invasive breast cancer in the presence of competing mortality: the Canadian National Breast Screening Study

  • Sharareh Taghipour
  • Dragan Banjevic
  • Joanne Fernandes
  • Anthony B. Miller
  • Neil Montgomery
  • Bart J. Harvey
  • Andrew K. S. Jardine
Epidemiology

Abstract

Mortality due to causes other than breast cancer is a potential competing risk which may alter the incidence probability of breast cancer and as such should be taken into account in predictive modelling. We used data from the Canadian National Breast Screening Study (CNBSS), which consist of two randomized controlled trials designed to evaluate the efficacy of mammography among women aged 40–59. The participants in the CNBSS were followed up for incidence of breast cancer and mortality due to breast cancer and other causes; this allowed us to construct a breast cancer risk prediction model while taking into account mortality for the same study population. In this study, we use 1980–1989 as the study period. We exclude the prevalent cancers from the CNBSS to estimate the probability of developing breast cancer, given the fact that women were cancer-free at the beginning of the follow-up. By the end of 1989, from 89,434 women, 944 (1.1 %) were diagnosed with invasive breast cancer, 922 (1.0 %) died from causes other than breast cancer, and 87,568 (97.9 %) were alive and not diagnosed with invasive breast cancer. We constructed a risk prediction model for invasive breast cancer based on 39 risk factors collected at the time of enrolment or the initial physical examination of the breasts. Age at entry (HR 1.07, 95 % CI 1.05–1.10), lumps ever found in left or right breast (HR 1.92, 95 % CI 1.19–3.10), abnormality in the left breast (HR 1.26, 95 % CI 1.07–1.48), history of other breast disease, family history of breast cancer score (HR 1.01, 95 % CI 1.00–1.01), years menstruating (HR 1.02, 95 % CI 1.01–1.03) and nulliparity (HR 1.70, 95 % CI 1.23–2.36) are the model’s predictors. We investigated the effects of time-dependent factors. The model is well calibrated with a moderate discriminatory power (c-index 0.61, 95 % CI 0.59–0.63); we use it to predict the 9-year risk of developing breast cancer for women of different age groups. As an example, we estimated the probability of invasive cancer at 5 years after enrolment to be 0.00448, 0.00556, 0.00691, 0.00863, and 0.01034, respectively, for women aged 40, 45, 50, 55, and 59, all of whom had never noted lumps in their breasts, had 32 years of menstruating, 1–2 live births, no other types of breast disease and no abnormality found in their left breasts. The results of this study can be used by clinicians to identify women at high risk of breast cancer for screening intervention and to recommend a personalized intervention plan. The model can be also utilized by a woman as a breast cancer risk prediction tool.

Keywords

Invasive breast cancer Competing mortality Risk prediction model 

Notes

Acknowledgments

We acknowledge the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Canadian Institute of Health Research (CIHR) of Canada for their financial support. We thank Dr. Elizabeth Thompson for proofreading the manuscript. We are thankful to the editor and two referees whose constructive comments have helped us to improve the presentation of the paper.

Conflict of interest

None is declared.

References

  1. 1.
    van Manen JG, Van Dijk PC, Stel VS, Dekker FW, Cleries M, Conte F, Feest T, Kramar R, Leivestad T, Briggs JD, Stengel B, Jager KJ (2007) Confounding effect of comorbidity in survival studies in patients on renal replacement therapy. Nephrol Dial Transplant 22(1):187–195PubMedCrossRefGoogle Scholar
  2. 2.
    Dd Rao VK, Iademarco EP, Fraser VJ, Kollef MH (1998) The impact of comorbidity on mortality following in-hospital diagnosis of tuberculosis. Chest 114(5):1244–1252CrossRefGoogle Scholar
  3. 3.
    Goodkin DA, Bragg-Gresham JL, Koenig KG, Wolfe RA, Akiba T, Andreucci VE, Saito A, Rayner HC, Kurokawa K, Port FK, Held PJ, Young EW (2003) Association of comorbid conditions and mortality in hemodialysis patients in Europe, Japan, and the United States: the Dialysis Outcomes and Practice Patterns Study (DOPPS). J Am Soc Nephrol 14(12):3270–3277PubMedCrossRefGoogle Scholar
  4. 4.
    Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886PubMedCrossRefGoogle Scholar
  5. 5.
    Wolbers M, Koller MT, Witteman JCM, Steyerberg EW (2009) Prognostic models with competing risks methods and application to coronary risk prediction. Epidemiology 20(4):555–561PubMedCrossRefGoogle Scholar
  6. 6.
    Mell LK, Jeong JH, Nichols MA, Polite BN, Weichselbaum RR, Chmura SJ (2010) Predictors of competing mortality in early breast cancer. Cancer 116(23):5365–5373PubMedCrossRefGoogle Scholar
  7. 7.
    Lughezzani G, Sun M, Shariat S, Budäus L, Thuret R, Jeldres C, Liberman D, Montorsi F, Perrotte P, Karakiewicz PI (2011) A population-based competing-risks analysis of the survival of patients treated with radical cystectomy for bladder cancer. Cancer 117(1):103–109PubMedCrossRefGoogle Scholar
  8. 8.
    Daskivich TJ, Chamie K, Kwan L, Labo J, Dash A, Greenfield S, Litwin MS (2011) Comorbidity and competing risks for mortality in men with prostate cancer. Cancer 117(20):4642–4650CrossRefGoogle Scholar
  9. 9.
    Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509CrossRefGoogle Scholar
  10. 10.
    Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD (2004) Minireview: a note on competing risks in survival data analysis. Br J Cancer 91(7):1229–1235PubMedCrossRefGoogle Scholar
  11. 11.
    Glynn RJ, Rosner B (2005) Comparison of risk factors for the competing risks of coronary heart disease, stroke, and venous thromboembolism. Am J Epidemiol 162(10):975–982PubMedCrossRefGoogle Scholar
  12. 12.
    Melberg T, Nygard OK, Kuiper KK, Nordrehaug JE (2010) Competing risk analysis of events 10 years after revascularization. Scand Cardiovasc J 44(5):279–288PubMedCrossRefGoogle Scholar
  13. 13.
    Forsblom C, Harjutsalo V, Thorn LM, Wadén J, Tolonen N, Saraheimo M, Gordin D, Moran JL, Thomas MC, Groop PH (2011) Competing-risk analysis of ESRD and death among patients with type 1 diabetes and macroalbuminuria. J Am Soc Nephrol 22(3):537–544PubMedCrossRefGoogle Scholar
  14. 14.
    Miller AB, Baines CJ, To T, Wall C (1992) Canadian national breast screening study—1. Breast cancer detection and death rates among women age 40–49 years. Can Med Assoc J 147(10):1459–1476Google Scholar
  15. 15.
    Miller AB, Baines CJ, To T, Wall C (1992) Canadian national breast screening study—2. Breast cancer detection and death rates among women age 50–59 years. Can Med Assoc J 147(10):1477–1488Google Scholar
  16. 16.
    Miller AB, To T, Baines CJ, Wall C (2002) The Canadian National Breast Screening Study-1: breast cancer mortality after 11 to 16 years of follow-up: a randomized screening trial of mammography in women age 40 to 49 years. Ann Intern Med 137(5 Part 1):305–312PubMedGoogle Scholar
  17. 17.
    Miller AB, To T, Baines CJ, Wall C (2002) Canadian National Breast Screening Study-2: 13-year results of a randomized trial in women aged 50–59 years. J Natl Cancer 192(18):1490–1499Google Scholar
  18. 18.
    Cui Y, Miller AB, Rohan TE (2006) Cigarette smoking and breast cancer risk: update of a prospective cohort study. Breast Cancer Res Treat 100(3):293–299PubMedCrossRefGoogle Scholar
  19. 19.
    Harvey BJ, Miller AB, Baines CJ, Corey PN (1997) Effect of breast self-examination techniques on the risk of death from breast cancer. Can Med Assoc J 157(9):1205–1212Google Scholar
  20. 20.
    Jain MG, Ferrence RG, Rehm JT, Bondy SJ, Rohan TE, Ashley MJ, Cohen JE, Miller AB (2000) Alcohol and breast cancer mortality in a cohort study. Breast Cancer Res Treat 64(2):201–209PubMedCrossRefGoogle Scholar
  21. 21.
    Schechter MT, Miller AB, Howe GR (1985) Cigarette smoking and breast cancer: a case control-study of screening program participants. Am J Epidemiol 121(4):479–487PubMedGoogle Scholar
  22. 22.
    Rohan TE, Howe GR, Friedenreich CM, Jain M, Miller AB (1993) Dietary fiber, vitamin A, C, and E, and risk of breast cancer: a cohort study. Cancer Cause Control 4(1):29–37CrossRefGoogle Scholar
  23. 23.
    Kabat GC, Miller AB, Jain M, Rohan TE (2007) Dietary iron and heme iron intake and risk of breast cancer: a prospective cohort study. Cancer Epidemiol Biomark Prev 16(6):1306–1308CrossRefGoogle Scholar
  24. 24.
    Schechter MT, Miller AB, Howe GR, Baines CJ, Craib KJ, Wall C (1989) Cigarette smoking and breast cancer: case control studies of prevalent and incident cancer in the Canadian National Breast Screening Study. Am J Epidemiol 130(2):213–220PubMedGoogle Scholar
  25. 25.
    Rohan TE, Jain MG, Howe GR, Miller AB (2000) Dietary folate consumption and breast cancer risk. J Natl Cancer 92(3):266–269CrossRefGoogle Scholar
  26. 26.
    Silvera SA, Jain M, Howe GR, Miller AB, Rohan TE (2006) Energy balance and breast cancer risk: a prospective cohort study. Breast Cancer Res Treat 97(1):97–106PubMedCrossRefGoogle Scholar
  27. 27.
    Rohan TE, Miller AB (1999) A cohort study of oral contraceptives and risk of benign breast disease. Int J Cancer 82(2):191–196PubMedCrossRefGoogle Scholar
  28. 28.
    Holowaty PH, Miller AB, Baines CJ, Risch H (1993) Canadian National Breast Screening Study: first screen results as predictors of future breast cancer risk. Cancer Epidemiol Biomark Prev 2:11–19Google Scholar
  29. 29.
    National Cancer Institute (2012) Breast cancer risk assessment tool. http://www.cancer.gov/bcrisktool/. Accessed 26 April 2012
  30. 30.
    Raghunathan TE, Solenberger PW, and Hoewyk JV (2012) IVEware: Imputation and Variance Estimation Software. 2010. http://www.isr.umich.edu/src/smp/ive/. Accessed 11 Jan 2012
  31. 31.
    US Preventive Services Task Force (2005) Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: recommendation statement. Ann Intern Med 143:355–361Google Scholar
  32. 32.
    Bakoyannis G, Touloumi G (2011) Practical methods for competing risks data: a review. Stat Methods Med Res. doi: 10.1177/0962280210394479 PubMedGoogle Scholar
  33. 33.
    Kalbfleisch JD, Prentice RL (1980) The statistical analysis of failure time data, 1st edn. Wiley, New YorkGoogle Scholar
  34. 34.
    Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail MH (2006) Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst 98(17):1215–1226PubMedCrossRefGoogle Scholar
  35. 35.
    Gail MH (2008) Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst 100(14):1037–1041PubMedCrossRefGoogle Scholar
  36. 36.
    Medicine World (2012) Epidemiology of breast cancer. http://medicineworld.org/cancer/breast/epidemiology-of-breast-cancer.html. Accessed 22 April 2012
  37. 37.
    Ahmed HG, Ali AS, Almobarak AO (2010) Frequency of breast cancer among Sudanese patients with breast palpable lumps. Indian J Cancer 47(1):48–51CrossRefGoogle Scholar
  38. 38.
    Weiss HA, Devesa SS, Brinton LA (1996) Laterality of breast cancer in the United States. Cancer Causes Control 7(5):539–543PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  • Sharareh Taghipour
    • 1
    • 2
  • Dragan Banjevic
    • 2
  • Joanne Fernandes
    • 3
  • Anthony B. Miller
    • 3
  • Neil Montgomery
    • 2
  • Bart J. Harvey
    • 3
  • Andrew K. S. Jardine
    • 2
  1. 1.Ryerson UniversityTorontoCanada
  2. 2.University of TorontoTorontoCanada
  3. 3.Dalla Lana School of Public HealthHealth Science BuildingTorontoCanada

Personalised recommendations