Browse > Article
http://dx.doi.org/10.7314/APJCP.2016.17.S3.5

Determination of a Change Point in the Age at Diagnosis of Breast Cancer Using a Survival Model  

Abdollahi, Mahbubeh (Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University)
Hajizadeh, Ebrahim (Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University)
Baghestani, Ahmad Reza (Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences)
Haghighat, Shahpar (Breast Cancer Research Center (BCRC), ACECR)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.17, no.sup3, 2016 , pp. 5-10 More about this Journal
Abstract
Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.
Keywords
Breast cancer; survival analysis; survival cure model; change point in age of diagnosis; cut-off point;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Akbari ME, Khayamzadeh M, Khoshnevis S, et al (2012). Five and ten years survival in breastcancer patients mastectomies vs. breast conserving surgeries personal experience. Iran J Cancer Prev, 1, 53-6.
2 Alarid F (2014). Calibration of piecewise markow models using a bayeslam change-point analysis through an iterative convex optimization algorithm. The 36th Annual Meeting of the Society for Medical Decision Making, Smdm.
3 Alieldin NH, Abo-Elazm OM, Bilal D, et al (2014). Age at diagnosis in women with non-metastatic breastcancer: Is it related to prognosis. J Egypt Natl Canc Inst, 26, 23-30.   DOI
4 Andres SA, Wittliff JL, Cheng A (2013). Protein tyrosine phosphatase 4A2 expression predicts overall and disease-free survival of human breastcancer and is associated with estrogen and progestin receptor status. Hormones and Cancer, 4, 208-21.   DOI
5 Assareh H, Mengersen K (2012). Change point estimation in monitoring survival time. Plos one, 7, 330-6.
6 Berrino F, De Angelis R, Sant M, et al (2009). Eurocare4. Survival of cancer patients diagnosed in 1995-99. Results and commentary. Eur J Cancer, 45, 91.
7 Buettner P, Garbe C, Guggenmoos-Holzmann I (1997). Problems in defining cutoff points of continuous prognostic factors: example of tumor thickness in primary cutaneous melanoma. Clin Epidemiol, 50, 1201-10.   DOI
8 Carlo JT, Grant MD, Knox SM, et al (2005). Survival analysis following sentinel lymph node biopsy: a validation trial demonstrating its accuracy in staging early breastcancer. Bayl Univ Med Cent, 18, 103-10.
9 Contal C, O’Quigley J (1999). An application of changepoint methods in studying the effect of age on survival in breastcancer. Comput Stat Data Anal, 30, 253-70.   DOI
10 Corbiere F, Commenges D, Taylor JM, et al (2009). A penalized likelihood approach for mixture cure models. Stat Med, 28, 510-24.   DOI
11 DeSantis C, Siegel R, Bandi P, et al (2011). Breastcancer statistics, 2011. CA Cancer J Clin, 61, 408-18.   DOI
12 Esserman LJ, Berry DA, Cheang MC, et al (2012). Chemotherapy response and recurrence-free survival in neoadjuvant breastcancer depends on biomarker profiles: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Breast Cancer Res Treat, 132, 1049-62.   DOI
13 Farewell VT (1982). The use of mixture models for the analysis of survival data with long-term survivors. Biometrics, 1041-6.
14 Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, et al (2013). Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. BMC Endocr Disord, 13, 47.   DOI
15 Gohari MR, Mahmoudi M, Mohammed K, et al (2006). Recurrence in breastcancer, Analysis with frailty model. Saudi Med J, 27, 1187-93.
16 Hanahan D, Weinberg RA (2000). The Hallmarks of Cancer Cancer Hallm, 100, 57-70.   DOI
17 Goldhirsch A, Gelber RD, Piccart-Gebhart MJ, et al (2013). 2 years versus 1 year of adjuvant trastuzumab for HER2-positive breastcancer (HERA): an open-label, randomised controlled trial. Lancet, 382, 1021-8.   DOI
18 Goodman MS, Li Y, Tiwari RC (2006). Survival analysis with change point hazard functions.
19 Hajian S, Vakilian K, Najabadi KM, et al (2011). Effects of education based on the health belief model on screening behavior in high risk women for breast cancer, Tehran, Iran. Asian Pac J Cancer Prev, 12, 49-54.
20 Heinzl H, Tempfer C (2001). A cautionary note on segmenting a cyclical covariate by minimum P-value search. Comput Stat Data Anal, 35, 451-61.   DOI
21 Heydari ST, Mehrabani D, Tabei S, et al (2012). Survival of breastcancer in southern Iran. Iran J Cancer Prev, 2, 51-4.
22 Howard-Anderson J, Ganz PA, Bower JE, et al (2012). Quality of life, fertility concerns, and behavioral health outcomes in younger breastcancer survivors: a systematic review. J Natl Cancer Inst, 104, 386-405.   DOI
23 Howlader N, Krapcho M, Garshell J, et al (2013). National cancer institute: surveillance, epidemiology, and end results. SEER Cancer Statistics Review, 1975-2010.
24 Lamont EB, Herndon JE, Weeks JC, et al (2006). Measuring disease-free survival and cancer relapse using Medicare claims from CALGB breastcancer trial participants (companion to 9344). J Natl Cancer Inst, 98, 1335-8.   DOI
25 Li C, Malone K, Porter P, et al (2003). Epidemiologic and molecular risk factors for contralateral breastcancer among young women. Br J Cancer, 89, 513-8.   DOI
26 Luong TM, Rozenholc Y, Nuel G (2013). Fast estimation of posterior probabilities in change-point analysis through a constrained hidden Markov model. Comput Stat Data Anal, 68, 129-40.   DOI
27 Li CI, (2010). Breastcancer epidemiology, Springer.
28 Li Y, Tiwari RC, Guha S (2007). Mixture cure survival models with dependent censoring. J R Stat Soc Series B (Stat Methodol), 69, 285-306.   DOI
29 Linden W, Vodermaier A, MacKenzie R, et al (2012). Anxiety and depression after cancer diagnosis: Prevalence rates by cancer type, gender, and age. J Affect Disord, 141, 343-51.   DOI
30 Machin D, Cheung YB, Parmar M (2006). Survival analysis: a practical approach, John Wiley and Sons.
31 Maggard MA, O'Connell JB, Lane KE, et al (2003). Do young breastcancer patients have worse outcomes. J Surg Res, 113, 109-13.   DOI
32 Maller RA, Zhou X (1996). Survival analysis with long-term survivors, Wiley New York.
33 Minicozzi P, Bella F, Toss A, et al (2013). Relative and disease-free survival for breastcancer in relation to subtype: a population-based study. J Cancer Res Clin Oncol, 139, 1569-77.   DOI
34 Othus M, Li Y, Tiwari R (2012). Change-point cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients. Appl Stat, 39, 901-11.   DOI
35 Parker W (2012). Uterine fibroids. Berek and Novak's Gynecology, 438-69.
36 Rosenberg J, Chia YL, Plevritis S (2005). The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breastcancer survival in the US SEER database. Breast Cancer Res Treat, 89, 47-54.   DOI
37 Wang JJ, Liew G, Klein R, et al (2007). Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations. Eur Heart J, 28, 1984-92.   DOI
38 Sertkaya D, Sozer MT (2003). A Bayesian approach to the constant hazard model with a change point and an application to breastcancer data. Hacet J Math Stat, 32, 33-41.
39 Taweab F, Ibrahim NA, Arasan J (2015). A bounded cumulative hazard model with a change-point according to a threshold in a covariate for right-censored data. Appl Math, 9, 69-74.
40 Vahdaninia M, Montazeri A (2004). Breast cancer in Iran: a survival analysis. Asian Pac J Cancer Prev, 5, 223-5.
41 Wingo PA, Ries LA, Parker SL, et al (1998). Long-term cancer patient survival in the United States. Cancer Epidemiol Biomarkers Prev, 7, 271-82.
42 Zafarghandi A, Harirchi I, Ebrahimi M, et al (1998). Breastcancer in Iran: A review of 3085 pathological records. Tehran Univ Med J, 56, 42-7.
43 Zahl PH, Tretls S (1997). Longterm survival of breastcancer in norway by age and clinical stage. Stat Med, 16, 1435-49.   DOI
44 Mokhtari Hesari P, Moghadami Fard Z, khodabakhshi R, et al (2014). Identification prognostic factors for disease-free survival in breastcancer patient. J North Khorasan Univ Med Sci, 5, 821-9.   DOI
45 Khodabakhshi R, Gohari M, Moghadamifard Z, Foadzi H, Vahabi N (2011). Disease-Free Survival of Breastcancer Patients and Identification of Related Factors. J Razi Uni Med Sci, 18, 27-33.
46 Yaghmaei S, BaniHashemi G, Ghorbani R (2008). Survival rate following treatment of primary breastcancer in Semnan, Iran (1991-2002). koomesh, 9, 111-6.