Modeling of Some Common Cancers in Rivers State of Nigeria, by Applying Markov Switching Intercept Vector Autoregressive (MSI-VAR) Model

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Published: 2023-04-12

Page: 117-129

Nnoka Love Cherukei *

Department of Statistics, Captain Elechi Amadi Polytechnic, Rumuola, P.M.B. 5936, Port Harcourt, Nigeria.

Ettuk, Ette Harrison

Department of Mathematics, Rivers State University, Nkpolu Oroworukwo, P.M.B. 5080, Port Harcourt, Nigeria.

*Author to whom correspondence should be addressed.


The study applied a non linear modeling technique; Markov Switching Vector Autoregressive (MS-VAR) model in modeling common cancers data in Rivers State of Nigeria. The data for the study was obtained from the Cancer Registry Unit, University of Port Harcourt Teaching Hospital. The study spanned from January, 2009 to December, 2020 consisting of 144 monthly observations. The objectives of the study may include ; to model and estimate the linear interdependence between the identified common cancers, to determine the direction of causality and the significance of causality among the study variables and lastly to determine the probability of transitioning from one state to the other and the duration of stay in a particular regime. The study ascertained the stationary conditions of the three variables. Breast cancer and Cervical cancers were stationary at levels while Prostate cancer was stationary after first difference.

The Augmented Dickey Fuller Unit Root Test was used to test for stationary behavior of the variables while Philip Peron Confirmatory test was applied. The study also tested for long-run relationship among the study variables using Johansen co-integration test which however indicated no long-run relationship according to Maximum- Eigen value and Trace test results. The stability of the model was tested by evaluating the inverse roots of the characteristic polynomial, since all the roots lied inside the unit circle, it was concluded that stability condition was satisfied.

Keywords: Common cancers, Markov switching intercept vector, metastasis

How to Cite

Cherukei, N. L., & Harrison, E. E. (2023). Modeling of Some Common Cancers in Rivers State of Nigeria, by Applying Markov Switching Intercept Vector Autoregressive (MSI-VAR) Model. Asian Research Journal of Current Science, 5(1), 117–129. Retrieved from


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World Health Organization Cancer; 2007 . Available:

Obiora CC, Abu EK. Breast cancer in Rivers State, Nigeria: ten-year review of the Port Harcourt cancer registry.SA Journal of Oncology. 2019;3.

Brinton Louise A, Figueroa D. Jonine, AwuahBaffour, Joel Yarney, Seth Wiafe, Shannon Wood, Daniel Ansong, Kofi Nyarko, Beatrice Wiafe-Addai& Nat Clegg-Lamptey. Breast cancer in Sub-Sahara Africa; Opportunities for Prevention. Breast Cancer Res Treat. 2014;144(3): 467-478.

Malara N, Leotta A, Sidota A, Lio S, D’ Angelo R, Caparello B. Aging, Hormonal Behavior and Cyclin DI in ductal Breast Carcinoma. Breast. 2006,15: 81-9.

Weinberg O, Marguez- Garban D, Pietras R. New Approaches to Reverse Resistance to Hormonal Therapy in Human Breast Cancer; Drug Resist. Update. 2005;8:219-33.

Mahmood MD, Alexandra L. Hanlon, Steven J. Feigenberg MD: Increasing National Mastectomy Rates for The Treatment of Early Stage Breast Cancer; Annals of Surgical Oncology. 2013; 20:1436-1443.

Walsh JC. The Impact of knowledge, Perceived Barriers and Perception of Risk on attendance for a Routine Cervical Smear; The European Journal of Contraception and Reproduction Health Care. 2006;11(4):291 -296

World Health Organization. Latest Global Cancer Data; 2020. Available:>health-topics

Krolzig Hans–Martin. Markov-Switching Vector Auto regression Modeling, Statistical Inference, and Application to Business Cycle Analysis: Lecture Notes in Economics & Mathematical System (LNE). 1997;454. ISBN:978-3-642-51684-9.

Kay Richard. A Markov Model for Analyzing Cancer Markers and Disease States in Survival Studies; Biometrics. Published by International Biometric Society. 1986;42(4):855 -865.

Pauker MD, Beck J Robert, Stephen. The Markov Process in Medical Prognosis: (Medical Decision Making. 1993;3:419-458.

Monbet V, Ailliot. Sparse Vector Markov Switching Auto regressive Model; Application to Multivariate Time Series of Temperature Computational Statistics and Data Analysis. 2017;8(40-51).

Clement MP, Krolzig HM. Can Oil Shocks Explain Asymmetries in US business cycle? : Empirical Economics. 2002;27(1): 185-204.

HaldrupNiels, Nielsen Franks & Morten Orregaard Nielsen: Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching; SSRN Electronic Journal; 2007. DOI.10.2139/SSRN.1150111

Lütkepohl H. Introduction to multiple Time series Analysis; New York; Springer. 2005;2.

Wai SP, Ismail MT, Sek SK. A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data: Information Management and Business Review. 2003; 5(8):379-384

Stevanovic A, Lee P, Wilcken N: Metastatic Breast Cancer; Aust, Fam Phys. 2006;35:309-11.

Ibrahim Akma Noor, Abdu Kudus, Isa Daud, Mohd R. Abubakar. Decision Tree for Competing Risks Survival Probability in Breast Cancer Study, International Journal of Biomedical Sciences. Volume 2004;3(1).

Cong Chunling, Tsokos, Chris P. Makov Modeling of Breast Cancer; Journal of Modern Applied Statistical Methods. 2009;8(2):626-631.

Hang J, Sim L, Zakaria Z. Non-invasive Breast Cancer Assessment using Magnetic Induction Spectroscopy Technique: Int. J Integr. Eng. 2017;9:15-20.

Inoue M, Nakagomi H, Nakada H, Furuya K, Ikegame K, Watanabe H. (2017): Specific Sites of Metastases in Invasive Lobular Carcinoma:A Retrospective Cohort Study of Metastatic Breast Cancer:Breast Cancer,2017; 20:1-6 24(5):667-672. JAMA.1995;273(7):548-552. DOI:10.1001/jama.1995.03520310046028

Ayumi Taguchi, Konan Hara, Jun Tomio, Kei Kawana, Tomoki Tanaka, Satoshi Baba, Akira Kawata, SatokoEguchi, Tetsushi Tsuruga, Mayuyo Mori, Katsuyuki Adachi, Takeshi Nagamatsu, Katsutoshioda, Toshiharu Yasugi, Yutaka Osuga & Tomoyuki Fujii: Multistate Markov Model to Predict the Prognosis of High Risk Human Papiliomavirus Related Cervical Lesion: Cancers. 2020;12(20): 270. Available:

Mengesha Ayelign, AntenehMessele, Biruk Beletew. Knowledge and Attitude Towards Cervical Among Reproductive Age Group Women in Gondar Town, North West Ethopia, BMC Public Health 20, Article Number. 2020;209(2020).

Helen IA, Anna M. Foss. A Comparative Analysis of Cervical Cancer Prevention in Nigeria and Nordic countries that have experienced a decline in Cervical Cancer incidence; International Health. 2021; 13(40):307 – 317.

Nyengidike TK, Oranu EO. Pattern of Cervical Cytology and High Risk Human Papilomavirus Strains in non HIV Positive Women Presenting for Cervical Cancer Screening in Port Harcourt Nigeria; Journal of Biosciences and Medicines. 2018;6: 68-76. Available:

Olufemi Ogunbiyi, Olayiwola B. Shittu. Increased Incidence of Prostate Cancer in Nigeria; Journal of the National Medical Association. 1999;91(3):159.

Steven A. Grover, Louis Coupal, HannaZowall, Raghu Rajan, John Trachtenberg, Mostafa Elhilali, Michael Chentner, Larry Goldenberg. The Clinical Burden of Prostate Cancer in Canada: Forecasts from the Montreal Prostate Cancer Model; 2000.

Melissa M. Center, Ahmedin Jemal, Joannie Lortet-Ticulent, Elizabeth Ward, Jacques Ferlay, Otis Brawley, Freddie Bray. International Variation in Prostate Cancer Incidence and Mortality Rates; European Urology. 2012;61(6) 1079- 1092.

Guidolin Massimo. Markov Switching Models in Empirical Finance; Working papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University; 2011.