Analyzing competing factors in Breast cancer by using Multistate model

Authors

  • Khanda Gharib Aziz Halabja Technical College for Applied Science, Sulaimani Polytechnic University, Sulaimaniyah, Kurdistan Region, Iraq
  • Abbas Gul MuradBeg Murad Statistic and Information Department, College of Administration and Economic, University of Sulaimani, Sulaimaniyah, Kurdistan Region, Iraq

DOI:

https://doi.org/10.32410/huj-10334

Keywords:

Multistate model, Markov Property, Cox Proportional Hazard Model, Breast Cancer, Competing factors

Abstract

Survival analysis can be defined as a field that studies the time period until the occurrence of an event. However, in some cases, these methods may not gain sufficient control over the disease process because disease progression may involve interesting intermediate events. Therefore, multistate model have multiple events or states, which can give greater knowledge and clarity of disease progression than a pure model for survival analysis. The main purpose of this study is to reduce the ambiguity of the multistate model theory that relies on the Markov property to estimate the competing factors that may have an impact on the amount of a patient’s surgery and to estimate the severity of transmission and the probabilities of transition between different cases (transient as well as absorption) of breast cancer patients. Finally, each factor has different effects on each transition.     

References

Ibe, O. (2013). Markov processes for stochastic modeling. Newnes. DOI: https://doi.org/10.1016/B978-0-12-407795-9.00015-3

Ibe, O. (2014) Fundamentals of applied probability and random processes.Academic Press. DOI: https://doi.org/10.1016/B978-0-12-800852-2.00012-2

Journal Papers:

Andersen, P. K., Hansen, L. S., & Keiding, N. (1991). Non-and semi-parametric estimation of transition probabilities from censored observation of a non-homogeneous Markov process. Scandinavian Journal of Statistics.

Broët, P., de la Rochefordière, A., Scholl, S. M., Fourquet, A., De Rycke, Y., Pouillart, P., ... & Asselain, B. (1999). Analyzing prognostic factors in breast cancer using a multistate model. Breast cancer research and treatment. DOI: https://doi.org/10.1023/A:1006197524405

Bradburn, M. J., Clark, T. G., Love, S. B., & Altman, D. G. (2003). Survival analysis part II: multivariate data analysis–an introduction to concepts and methods. British journal of cancer. DOI: https://doi.org/10.1038/sj.bjc.6601119

Doutani, H., Doutani, J., Parveen, S., Khan, F., Baloch, D. M., & Roohullah, H. K. N. A. (2012). Clinical and histopathological characteristics of breast cancer in people of baluchistan and adjoining territory of afghanistan. Current Research Journal of Biological Sciences.‏

De Bock, G. H., Putter, H., Bonnema, J., Van Der Hage, J. A., Bartelink, H., & Van De Velde, C. J. (2009). The impact of loco-regional recurrences on metastatic progression in early-stage breast cancer: a multistate model. Breast cancer research and treatment. DOI: https://doi.org/10.1007/s10549-008-0300-2

de Wreede, L. C., Fiocco, M., & Putter, H. (2011). mstate: an R package for the analysis of competing risks and multi-state models. Journal of statistical software. DOI: https://doi.org/10.18637/jss.v038.i07

Grover, G., Swain, P. K., Goel, K., & Singh, V. (2018). Multistate Markov Modelling for Disease Progression of Breast Cancer Patients Based on CA15-3 Marker. Thailand Statistician.

Kay, R. (1982). The analysis of transition times in multistate stochastic processes using proportional hazard regression models. Communications in Statistics-Theory and Methods. DOI: https://doi.org/10.1080/03610928208828346

Meira-Machado, L., de Uña-Álvarez, J., Cadarso-Suarez, C., & Andersen, P. K. (2009). Multi-state models for the analysis of time-to-event data. Statistical methods in medical research. DOI: https://doi.org/10.1177/0962280208092301

Putter, H., van der Hage, J., de Bock, G. H., Elgalta, R., & van de Velde, C. J. (2006). Estimation and prediction in a multi‐state model for breast cancer. Biometrical Journal: Journal of Mathematical Methods in Biosciences. DOI: https://doi.org/10.1002/bimj.200510218

Saint-Pierre, P., Combescure, C., Daures, J.P., Godard, P., (2003).The analysis of asthma control under a markov assumption with use of covariates. Statistics in Medicine. DOI: https://doi.org/10.1002/sim.1680

Websites:

Annual Report Iraqi Cancer Registry (2015-2016).Republic of Health Ministry of HealthEnvironment. Iraqi Cancer Board.

Published

2020-09-30

How to Cite

Aziz, K. G. ., & Murad, A. G. M. . (2020). Analyzing competing factors in Breast cancer by using Multistate model. Halabja University Journal, 5(3), 378-397. https://doi.org/10.32410/huj-10334

Similar Articles

1-10 of 55

You may also start an advanced similarity search for this article.