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Breast Cancer Research and Treatment

, Volume 89, Issue 2, pp 173–178 | Cite as

Hazard rates of recurrence following diagnosis of primary breast cancer

  • Ismail Jatoi
  • Anna Tsimelzon
  • Heidi Weiss
  • Gary M. Clark
  • Susan G. Hilsenbeck
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Abstract

We calculated hazard rates for recurrence in patients with primary breast cancer (stage I, II; no adjuvant therapy). Previous publications have indicated a peak in hazard rates for recurrence (or death) at approximately 2–3 years after diagnosis of primary breast cancer. However, there have been conflicting reports concerning the presence of a second peak at 5–7 years after diagnosis. In this study, we estimated hazard functions by the Nelson–Aalen method and fit by cubic–linear and cubic–cubic–linear models to test for the presence of one or two peaks, respectively. We identified two peaks in hazard of recurrence, one at 2 years and another at 5 years. The 5-year peak, though statistically significant, represents very small differences in patient outcome. This additional peak may be an artifact of interval censoring due to a tendency to follow-up patients at specific bench-mark time points.

Keywords

breast cancer hazard functions hazard rates recurrence survival 

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References

  1. Kay, R, Scheurlen, H, Schumacher, M 1982On the use of hazard functions in breast cancer studiesExperientia Suppl41118130Google Scholar
  2. Simes, RJ, Zelen, M 1985Exploratory data analysis and the use of the hazard function for interpreting survival data: an investigator’s primerJ Clin Oncol314181431Google Scholar
  3. Baum, M, Badwe, RA 1994Does surgery influence the natural history of breast cancerJohnson, H,Jr eds. Breast Cancer: Controversies in ManagementFutura Publishing Company, Inc.,Armonk, NY6169Google Scholar
  4. Yakovlev, AY, Tsodikov, AD, Boucher, K, Kerber, R 1999The shape of the hazard function in breast carcinoma: curability of the disease revisitedCancer8517891798Google Scholar
  5. Demicheli, R, Abbattista, A, Miceli, R, Valagussa, P, Bonadonna, G 1996Time distribution of the recurrence risk for breast cancer patients undergoing mastectomy: further support about the concept of tumor dormancyBreast Cancer Res Treat41177185Google Scholar
  6. Demicheli, R, Miceli, R, Brambilla, C, Ferrari, L, Moliterni, A, Zambetti, M, Valagussa, P, Bonadonna, G 1999Comparative analysis of breast cancer recurrence risk for patients receiving or not receiving adjuvant cyclophosphamide, methotrexate, fluorouracil (CMF). Data supporting the occurrence of ‘cures’Breast Cancer Res Treat53209215Google Scholar
  7. Demicheli, R, Valagussa, P, Bonadonna, G 2002Double-peaked time distribution of mortality for breast cancer patients undergoing mastectomyBreast Cancer Res Treat75127134Google Scholar
  8. Gasparini, G, Biganzoli, E, Bonoldi, E, Morabito, A, Fanelli, M, Boracchi, P 2001Angiogenesis sustains tumor dormancy in patients with breast cancer treated with adjuvant chemotherapyBreast Cancer Res Treat657175Google Scholar
  9. Karrison, TG, Ferguson, DJ, Meier, P 1999Dormancy of mammary carcinoma after mastectomyJ Natl Cancer Inst918085Google Scholar
  10. Saphner, T, Tormey, DC, Gray, R 1996Annual hazard rates of recurrence for breast cancer after primary therapyJ Clin Oncol1427382746Google Scholar
  11. Collett, D 2003Modelling survival data in medical researchChapman & Hall/CRCBoca Raton33Google Scholar
  12. Nelson, W 1969Hazard plotting for incomplete dataJ Qual Technol12752Google Scholar
  13. Hess, KR, Serachitopol, DM, Brown, BW 1999Hazard function estimators: a simulation studyStat Med1830753088Google Scholar
  14. Efron, B 1988Logistic regression, survival analysis and the Kaplan–Meyer curveJ Am Stat Assoc83414425Google Scholar
  15. Durrleman, S, Simon, R 1989Flexible regression models with cubic splinesStat Med8551561Google Scholar
  16. Demicheli, R, Miceli, R, Valagussa, P, Bonadonna, G 2000Re: Dormancy of mammary carcinoma after mastectomyJ Natl Cancer Inst92347348Google Scholar
  17. Demicheli, R, Bonadonna, G, Hrushesky, WJ, Retsky, MW, Valagussa, P 2004Menopausal status dependence of early mortality reduction due to diagnosis of smaller breast cancers (T1 v T2–T3): relevance to screeningJ Clin Oncol22102107Google Scholar
  18. O’Reilly, MS, Holmgren, L, Shing, Y, Chen, C, Rosenthal, RA, Moses, M, Lane, WS, Cao, Y, Sage, EH, Folkman, J 1994Angiostatin: a novel angiogenesis inhibitor that mediates the suppression of metastases by a Lewis lung carcinomaCell79315328CrossRefPubMedGoogle Scholar
  19. Los, M, Voest, EE 2001The potential role of antivascular therapy in the adjuvant and neoadjuvant treatment of cancerSemin Oncol2893105Google Scholar
  20. Eckhardt, SG, Pluda, JM 1997Development of angiogenesis inhibitors for cancer therapyInvest New Drugs1513Google Scholar
  21. Greer, S, Morris, T, Pettingale, KW 1979Psychological response to breast cancer: effect on outcomeLancet2785787Google Scholar
  22. Watson, M, Haviland, JS, Greer, S, Davidson, J, Bliss, JM 1999Influence of psychological response on survival in breast cancer: a population-based cohort studyLancet35413311336CrossRefPubMedGoogle Scholar
  23. Cox, DR 1972Regression models and life tables (with discussion)J R Stat Soc B74187220Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Ismail Jatoi
    • 1
  • Anna Tsimelzon
    • 2
  • Heidi Weiss
    • 2
  • Gary M. Clark
    • 3
  • Susan G. Hilsenbeck
    • 2
  1. 1.Department of SurgeryUniformed Services UniversityBethesdaMaryland
  2. 2.Breast Center at Baylor College of MedicineHouston
  3. 3.OSI Pharmaceuticals, Inc.BoulderUSA

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