Bayesian Generalized Linear Mixed Modeling of Breast Cancer

  • Ogunsakin ROPO EBENEZER Department of Statistics, School of Mathematics, Statistics, and Computer Science, University of Kwazulu Natal, Dur-ban,
  • Siaka LOUGUE Department of Statistics, School of Mathematics, Statistics, and Computer Science, University of Kwazulu Natal, Dur-ban, South Africa
Keywords: Bayesian, Breast cancer, Multilevel, Generalized linear mixed modeling, CODA/BOA


Background: Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria. Methods: The data was collected for 247 women between years 2011-2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect. Results: The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35-49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type. Conclusion: Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method.     


1. Ebughe G, Ekanem I, Omoronyia O et al (2013). Age specific incidence of breast cancer in calabar, nigeria. Int J Trop Dis, 16:1–12
2. Chen H, Zhou M-q, Tian W et al (2016). Ef-fect of age on breast cancer patient prog-noses: A population-based study using the seer 18 database. PLoS One, 11(10):e0165409.
3. Ferlay J, Shin H-R, Bray F et al (2010). Esti-mates of worldwide burden of cancer in 2008: Globocan 2008. Int J Can-cer, 127(12):2893-917.
4. Torre LA, Bray F, Siegel RL et al (2015). Global cancer statistics, 2012. CA Cancer J Clin, 65(2):87-108.
5. Akarolo-Anthony SN, Ogundiran TO, Adebamowo CA (2010). Emerging breast cancer epidemic: evidence from af-rica. Breast Cancer Res, 12 Supple 4:S8. doi: 10.1186/bcr2737.
6. Popoola AO, Omodele FO, Oludara MA et al (2013). Prevalence and pattern of can-cers among adults attending a tertiary health institution in lagos, nigeria. IOSR J Dental Med Sci, 6(3):68–73.
7. Olugbenga AM, Olanrewaju MJ, Kayode OM (2012). Profile of cancer patients at-tending tertiary health institutions in southwestern nigeria. Asian J Pharm Clin Res, 5:34–7.
8. Townsley CA, Selby R, Siu LL (2005). Sys-tematic review of barriers to the recruit-ment of older patients with cancer onto clinical trials. J Clin Oncol, 23(13):3112–3124.
9. Biganzoli L, Wildiers H, Oakman C et al (2012). Management of elderly patients with breast cancer: updated recommen-dations of the international society of geriatric oncology (siog) and european society of breast cancer specialists (euso-ma). Lancet Oncol, 13(4):e148-60.
10. Mieog JSD, de Kruijf EM, Bastiaannet E et al (2012). Age determines the prognostic role of the cancer stem cell marker alde-hyde dehydrogenase-1 in breast cancer. BMC Cancer, 12:42
11. Kiderlen M, Walsh PM, Bastiaannet E et al (2015). Treatment strategies and survival of older breast cancer patients–an inter-national comparison between the nether-lands and ireland. PLoS One, 10(2):e0118074.
12. Jolly TA (2014). External validity of a trial comprised of elderly patients with hor-mone receptor-positive breast cancer. Breast Diseases, 25(4):307–308.
13. Zare N, Haem E, Lankarani KB et al (2013). Breast cancer risk factors in a defined population: weighted logistic regression approach for rare events. J Breast Cancer, 16(2):214-9.
14. Gelman A, Hill J (2006). Data analysis using re-gression and multilevel/hierarchical models. Cambridge university press.
15. Ntzoufras I (2011). Bayesian modeling using WinBUGS, volume 698. John Wiley & Sons.
16. Ngesa O, Mwambi H, Achia T (2014). Bayesian spatial semiparametric modeling of hiv variation in kenya. PLoS One, 9(7):e103299.
17. Gilks, Roberts G (1996). Strategies for im-proving mcmc. In, markov chain Monte Carlo in practice (gilks, wr, richardson, s., spiegelhalter, dj eds).
18. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000). Winbugs-a bayesian modelling framework: concepts, structure, and ex-tensibility. Stat Comput, 10:325-337.
19. Cleries R, Ribes J, Buxo M et al (2012). Bayesian approach to predicting cancer incidence for an area without cancer reg-istration by using cancer incidence data from nearby areas. Statist Med, 31(10):978-87.
20. Gelman A Carlin JB, Stern HS, Rubin DB (2014a). Bayesian data analysis, volume 2. Chapman & Hall/CRC Boca Raton, FL, USA.
21. Toft N, Innocent GT, Gettinby G, Reid SW (2007). Assessing the convergence of markov chain Monte Carlo methods: an example from evaluation of diagnostic tests in absence of a gold standard. Prev Vet Med, 79(2-4):244-56.
22. Odetunmibi O, Adejumo A and Sunday OO (2013). Loglinear modelling of can-cer patients cases in nigeria: An explora-tory study approach. Loglinear modelling of cancer patients cases in Nigeria: An exploratory study approach, pages 1–5.
23. Cluze C, Colonna M, Remontet L et al (2009). Analysis of the effect of age on the prognosis of breast cancer. Breast Cancer Res Treat, 117:121–129.
24. Ibrahim A, Salem M, Hassan R (2014). Out-come of young age at diagnosis of breast cancer in south egypt. Gulf J On-colog, 1(15):76-83.
25. Alieldin NH, Abo-Elazm OM, Bilal D et al (2014). Age at diagnosis in women with non-metastatic breast cancer: Is it related to prognosis? J Egypt Natl Canc Inst, 26(1):23-30.
26. Brandt J, Garne JP, Tengrup I, Manjer J (2015). Age at diagnosis in relation to survival following breast cancer: a cohort study. World J Surg Oncol, 13:33.
27. Adebamowo CA, Ajayi O (2000). Breast cancer in nigeria. West Afr J Med, 19(3):179-91.
28. Ikpat O, Ndoma-Egba R, Collan Y (2002). Influence of age and prognosis of breast cancer in nigeria. East Afr Med J, 79(12):651-7.
29. Akinyemiju TF, Pisu M, Waterbor JW, Al-tekruse SF (2015). Socioeconomic status and incidence of breast cancer by hor-mone receptor subtype. Springerplus, 4:508.
30. Schonberg MA, Marcantonio ER, Li D et al (2010). Breast cancer among the oldest old: tumor characteristics, treatment choices, and survival. J Clin On-col, 28(12):2038-45.
31. Jackman S (2000). Estimation and inference via bayesian simulation: An introduction to markov chain Monte Carlo. Am J Pol Sci, 44(2): 375-404.
32. Abudu E, Banjo A, Izegbu M et al (2007). Malignant breast lessions at olabisiona-banjo university teaching hospital (oouth), sagamu-a histopathological review. Niger Postgrad Med J, 14(1):57–59.
33. Arora N, Simmons RM (2009). Malignant breast disease: Diagnosis and assess-ment. General Surgery, pages 1481–1494.
34. Yüksel S, Altun Uğraş G, Çavdar İ et al (2017). A risk assessment comparison of breast cancer and factors affected to risk perception of women in turkey: A cross-sectional study. Iran J Public Health, 46(3):308-317.
35. Oladimeji KE, Tsoka-Gwegweni JM, Igbodekwe FC et al (2015). Knowledge and beliefs of breast self-examination and breast cancer among market women in ibadan, south west, ni-geria. PLoS One, 10(11):e0140904.
36. Jebbin N, Adotey J (2004). Attitudes to, knowledge and practice of breast self-examination (BSE) in Port Harcourt. Ni-ger J Med, 13(2):166–170.
37. Omaka-Amari LN, Ilo CI, Nwimo IO et al (2015). Demographic differences in the knowledge of breast cancer among women in ebonyi state, nigeria. Internation-al Journal of Nursing, Midwife and Health Re-lated Cases, 1(3):18–27.
38. Ozturk M, Engin V, Kisioglu A, Yilmazer G (2000). Effects of education on knowledge and attitude of breast self-examination among 25+ years old wom-en. Eastern J Med, 5(1):13–17.
39. Popoola AO, Wright KO, Igwilo AI et al (2013). Literacy and Breast Cancer Diag-nosis and Treatment among Patients in a Tertiary Health Institution of Lagos Nige-ria. IOSR J Dental Med Sci, 5(4):49-54.
40. Hussain SK, Altieri A, Sundquist J, Hem-minki K (2008). Influence of education level on breast cancer risk and survival in sweden between 1990 and 2004. Int J Cancer, 122(1):165-9.
41. Ibrahim N, Oludara M (2012). Socio-demographic factors and reasons associated with delay in breast cancer presenta-tion: a study in Nigerian women. Breast, 21(3):416-8.
42. Zare N, Khodarahmi S, Rezaianzadeh A (2015). The role of prognostic factors on the survival of breast cancer patients: Bayesian approach. Iranian Journal of Epi-demiology, 11(3):23–33.
How to Cite
ROPO EBENEZER O, LOUGUE S. Bayesian Generalized Linear Mixed Modeling of Breast Cancer. Iran J Public Health. 48(6):1043-1051.
Original Article(s)