Chapter 4: RESULTS 69
4.4 DIAGNOSTIC DETERMINANT OF PREFERRED CANCER TREATMENT 87
framework to breast cancer data. Both the classical approach and Bayesian approach suggest that age of the patients and those with at least high school education are at higher risk of being diagnosed with malignant breast lesion than benign breast le- sion in Western Nigeria. A comparison of the classical and Bayesian approach to modeling breast cancer reveals lower standard errors of the estimated coefficients in the Bayesian approach in the setting of generalized linear mixed model. Thus, the Bayesian approach is more stable. Malignant breast lesion is increasing alarmingly even though most people lack basic knowledge about its spread. The higher propor- tion of those affected by malignant breast lesion is found among the educated and younger women. Therefore, this shows that non-educated women do not patron- ize these services based on our findings. More efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast le- sions can be reduced, especially among women.
We recommend that governments, non-governmental organizations and other sec- tors involved in policy making to put in place policies, strategies and sensitization that target non-educated women to enhance their patronization of breast cancer screening in the health facilities, so as to access the appropriate management health assessment as well as providing financially supported treatments for breast cancer patients.
4.4 DIAGNOSTIC DETERMINANT OF PREFERRED CAN-
The mean age of the patients (at the time of diagnosis) was 42.2 years with 52% of the women aged between 35-49 years in this study. Bayesian analysis showed a signif- icant relationship between treatment modality and the age (P=0.04), and malignant breast lesion (P=0.001). The result also indicate that hospital a breast cancer patient attends in this part of Nigeria also played a significant role for the treatment modal- ity.By fitting Bayesian multilevel models the variables age and breast cancer stages (malignant lesion) were significant.
The results showed that age, breast cancer stages (malignant) and hospital had a sig- nificant role in the treatment modality of breast cancer patients in Western Nigeria.
The study showed the practicality and flexibility of Bayesian multilevel approach in analyzing breast cancer data.
Keywords: Bayesian, Bayesian inference, Multilevel, Generalized linear mixed mod- eling, CODA/BOA.
4.4.1 Introduction
Breast cancer refers to a malignant tumor that has developed from cells in the breast.
Breast cancer cells are normal cells that at some point begin to present structural or functional alterations in nuclear deoxyribonucleic acid (DNA) and gradually became abnormal due to uncontrolled cell division. This uncontrolled division leads to the cells growing at an abnormal, uncontrolled rate. Breast cancer is the most common cause of malignancy among women worldwide (Ebughe et al., 2013a; Adebamowo
& Ajayi, 1999; Chen et al., 2016), and is a public health challenges among Nigeria women. Although cancer of the breast is thought to be a disease of the developed world, literature reveals that almost 58% of deaths that occur as a result of breast can- cer are traced to less developed countries (Ferlay et al., 2010). Available record by World Health Organization indicated that 30 Nigerian women died of breast cancer every day in 2008 and this had risen to 40 women in 2012. Statistics show that over 508 000 of Nigerian women died in 2011 as a result of breast cancer (Torre et al., 2015;
Akarolo-Anthony et al., 2010; AO et al., 2013; Adebamowo & Ajayi, 1999). Moody et.al (OLUGBENGA et al., 2012) presented a profile of cancer patients attending ter- tiary health institution in South Western parts of the country. They observed the occurrence and distribution of cancers among the southwestern citizens, it was con- cluded that breast cancer alone accounted for 37% of all the cancer cases present in southwestern of the country.
With advancement in medical technology, treatment for breast cancer advances have been made regardless of the site of the cancer in Nigeria. Better treatment for breast
cancer patients is difficult to define and the literature has revealed that older women are sometimes excluded from clinical treatment trials, probably because of their age (Townsley et al., 2005). Since breast cancer biology differs from patient to patient with respect to factors like age, variation in response to treatment, and substantial competing risks of mortality (Biganzoli et al., 2012; Mieog et al., 2012; Kiderlen et al., 2015), the exclusion of some patients might not be valid. This implies that those elderly who are included in trials are probably not a true representation for the gen- eral older population (Jolly, 2015). Consequently, an evidence-based treatment strat- egy for women with breast cancer is needed. Literature review on epidemiological studies of risk factors for breast cancer have reported that breast cancer is related to family history of breast cancer, early menstruation, late onset of menopause, old age, age at first pregnancy over 30 years, use of contraceptives, hormonal treatment after menopause, no history of breastfeeding and obesity (Zare et al., 2013).
Although there have been substantial published studies on prognostic factors for breast cancer in western Nigeria, population-based research is sparse. Therefore, we sought to determine risk factors for treatment given to female breast cancer in western Nigeria using generalized linear mixed models. This research focuses on the justification for the use of the conventional surgery method in the treatment of cancer, based on data from two understudied hospitals in Southwestern Nigeria, one federally owned, and the other state-owned. Consequently, the questions in the minds of people in this region regarding the method of breast cancer treatment em- ployed by federal hospitals, as against that used by state hospitals, will have been addressed. Therefore, it is important to explore the relationship between these vari- ables and treatment modality. Fundamentally, this study presents a comparison of the classical and Bayesian multivariate generalized linear mixed models.
4.4.2 Materials and Methods 4.4.3 Ethics
Ethical clearance to conduct the study was sought from the Ethical Review Commit- tee of the Federal Teaching Hospital, Ekiti State, Nigeria. The data was extracted from the cancer registry of the Federal Medical Teaching Hospital. 237 records and 20 variables of breast cancer data were accessed, each containing patient-related tu- mor information. Extensive variable selection procedures were performed on the 20 variables, and the records of patients aged 20 years and above were selected for the analysis. The information collected included age, marital status, educational level, religion, race, type of breast cancer, occupation, Lab number, case number, site of the cancer, type of diagnosis, and histological type. With respect to the quality
of the data obtained, the main concern was the proportion of hospital records in which some of the relevant variables were absent. Information relating to variables like weight, height, age at first full term pregnancy, and age at menopause were missing. Other information recorded was the type of treatment received: surgery, chemotherapy, hormonal therapy, radiotherapy or a combination of these. For input variable selection, we tried to limit the number of variables and select only the clin- ically relevant ones. Exact Logistic regression models were fitted to obtain indepen- dent estimates of the risk of breast cancer. Modeling started with all the variables, followed by sequential deletion according to their statistical importance. SAS was used for classical statistical analysis, while WinBUGS was used for Bayesian anal- ysis. A GLMM with a logit link function was performed for both classical as well as Bayesian method since this study considered two levels of analyses (hospitals).
We also examine several diagnostics that have a very wide range of application. The diagnostics are those of Geweke (Geweke et al., 1991), Heidelberger and Welch (Hei- delberger & Welch, 1983), and Raftery and Lewis (Raftery & Lewis, 1992), which look at convergence of an individual chain, and that of Gelman and Rubin (Gelman
& Rubin, 1992), which bases convergence on analysis of multiple chains.