Abstract

Cervical cancer is the third most common malignancy in women worldwide, and in developing countries, it remains a leading cause of cancer-related death for women. The disease is a significant illness in women in Sub Sahara Africa Countries like Nigeria. Cervical cancer ranks as the 2^nd most prevalent cancer among women in Nigeria and the most prevalent cancer among ages 15 - 44 years. Some of the identified risk factors of this disease have been wrongly captured in some statistical models. This consequentially makes the result of the analysis wrongly interpreted and concluded when the risk factors are continuous. This study is aimed at examining the Bayesian estimation of cervical cancer on a woman’s age and survival period of the disease using the application of non-parametric techniques. The study is a means to apply Bayesian Nonparametric estimation on cervical cancer. The study findings reveal that women between ages 40 - 60 years have a significant increase in the probability of mortality from cervical cancer when the survival period is between stage I-IV. Also, there is an increase in woman’s age and survival period of living with the disease which is significantly likely to decrease the mortality from cervical cancer in the study area.

Keywords: Bayesian Estimation; Mortality; Risk Factors; Probability; Survival Time

References

1. Kanavos P. “The rising burden of cancer in the developing world”. Annals of Oncology 17 (2006): 15-23.
2. Stewart BW and Kleigues P. “World Cancer Report”. IARC Press; Lyon France (2003).
3. World Health Organization (WHO). Human papillomavirus infection and cervical cancer (2010).
4. Marc A., et al. “Estimates of incidence and mortality of cervical cancer in 2018: A Worldwide Analysis (2020).
5. World Health Organization (WHO). Cervical cancer (2019).
6. Yahya A and Mande AT. “Awareness and knowledge of cervical cancer and its screening methods among women attending primary healthcare centers in Zaria, North-Western, Nigeria” (2019).
7. Eilers PHC and Marx BD. “Flexible smoothing using B-splines and penalized likelihood (with comments and rejoinder)”. Statistical Science 11 (1996): 89-121.
8. Holmes C and Held L. “Bayesian auxiliary variable models for binary and multinomial regression”. Bayesian Analysis 1 (2006): 145-168.
9. Lawson A. “Bayesian disease mapping: Hierarchical modeling in spatial Epidemiology”. Andrew B. Lawson. P; Cm. -- (Chapman and Hall/CRC Interdisciplinary Statistics Series) includes Bibliographical References and Index (2008).
10. Fahrmeir L and Lang S. “Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors”. Journal of the Royal Statistical Society C 50 (2001): 201-220.
11. Bray F., et al. “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”. CA: A Cancer Journal for Clinicians 68 (2018): 394-424.
12. Brezger A and Lang S. “Generalized Structured Additive Regression Based on Bayesian P-Splines”. Computational Statistics and Data Analysis 50 (2006): 947-991.
13. Parkin DM., et al. “Cancer incidence in Five Continents Lyon: IARC Scientific Publications 7 (1997): 13-14.
14. Siegel RL., et al. “Cancer statistics, 2015”. CA: A Cancer Journal for Clinicians 65 (2015): 5-29.
15. “Cancer” (2007a).
16. WHO calls for prevention of cancer through healthy workplaces (2007b).
17. https://emedicine.medscape.com/article/253513-overview
18. https://www.hpvcentre.net/statistics/reports/NGA_FS.pdf