Evaluation of the Effect of Prognostic Variables on the Survival Analysis of Prostate Cancer

Abeysekera W., Sooriyarachchi M. (2009): Use of Schoenfeld's global test to test the proportional hazards assumption in the Cox proportional hazards model: an application to a clinical study. Journal of the National Science Foundation of Sri Lanka 37(1): 41–51.10.4038/jnsfsr.v37i1.456Search in Google Scholar

Akaike H. (1974): A new look at the statistical model identification. IEEE transactions on automatic control 19(6): 716–723.10.1109/TAC.1974.1100705Search in Google Scholar

Ansin E. (2015): An evaluation of the Cox–Snell residuals.Search in Google Scholar

Baade P.D., Youlden D.R., Krnjacki L.J. (2009): International epidemiology of prostate cancer: geographical distribution and secular trends. Molecular Nutrition and Food Research 53(2): 171–184.10.1002/mnfr.20070051119101947Search in Google Scholar

Bellah S.F., Salam M.A., Billah S.S., Karim M.R. (2022): Genomewide association study of cyp3a4 and cyp3a5 genes in relation to the risk of prostate cancer among Bangladeshi population.10.21203/rs.3.rs-1681214/v1Search in Google Scholar

Bitew Z.W., Ayele E.G., Worku T., Alebel A., Alemu A., Worku F., Yesuf A. (2021): Determinants of mortality among under-five children admitted with severe acute malnutrition in Addis Ababa, Ethiopia. Nutrition Journal 20(1): 1–15.10.1186/s12937-021-00750-0869100934930311Search in Google Scholar

Bland J.M., Altman D.G. (2004): The logrank test. British Medical Journal (BMJ) 328(7447): 1073.10.1136/bmj.328.7447.107340385815117797Search in Google Scholar

Bozdogan and Hamparsum (2003). Statistical data mining and knowledge discovery. CRC Press p.18610.1201/9780203497159Search in Google Scholar

Byar D., Green S. (1980): The choice of treatment for cancer patients based on covariate information. Bulletin du Cancer 67(4): 477–490.Search in Google Scholar

Cox D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological) 34(2), 187–220.10.1111/j.2517-6161.1972.tb00899.xSearch in Google Scholar

Esper P., Mo F., Chodak G., Sinner M., Cella D., Pienta K.J. (1997): Measuring quality of life in men with prostate cancer using the functional assessment of cancer therapy-prostate instrument. Urology 50(6): 920–928.10.1016/S0090-4295(97)00459-79426724Search in Google Scholar

Harrell Jr F.E. (2015): Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.10.1007/978-3-319-19425-7Search in Google Scholar

Hussain S.M.A. (2013): Comprehensive update on cancer scenario of Bangladesh. South Asian Journal of Cancer 2(4): 279.10.4103/2278-330X.119901388906224455659Search in Google Scholar

Jemal A., Siegel R., Ward E., Hao Y., Xu J., Thun M.J. (2009): Cancer statistics. CA: A cancer journal for clinicians 59(4): 225–249.10.3322/caac.20006Search in Google Scholar

Kaplan E.L., Meier P. (1958): Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53(282): 457–481.10.1080/01621459.1958.10501452Search in Google Scholar

Kassambara A., Kassambara M.A. (2020): Package ‘ggpubr’. R package version 0.4.0.Search in Google Scholar

Kassambara A., Kosinski M., Biecek P., Fabian S. (2017): Package ‘survminer’. Drawing Survival Curves using ‘ggplot2’. R package version 0.4.9.Search in Google Scholar

Kay R. (1986): Treatment effects in competing-risks analysis of prostate cancer data. Biometrics 42(1): 203—211.10.2307/2531258Search in Google Scholar

Lawless J.F., 2011: Statistical models and methods for lifetime data. John Wiley & Sons.Search in Google Scholar

Lunn M., McNeil D. (1995): Applying Cox regression to competing risks. Biometrics 51(2): 524–532.10.2307/2532940Search in Google Scholar

Mantel N. (1966): Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50: 163–170.Search in Google Scholar

Moul J.W., Douglas T.H., McCarthy W.F., McLeod D. G. (1996): Black race is an adverse prognostic factor for prostate cancer recurrence following radical prostatectomy in an equal access health care setting. The Journal of Urology 155(5): 1667–1673.10.1016/S0022-5347(01)66160-3Search in Google Scholar

Team R. C. (2000). R language definition. Vienna, Austria: R Foundation for Statistical Computing.Search in Google Scholar

Shah S.A., Ismail N., Taib S., Mat S.N., Safian N. (2021): Survival analysis and prognostic factors for prostate cancer patients at Universiti Kebangsaan Malaysia medical centre, Kuala Lumpur. Sains Malaysiana 50(5): 1367–1379.10.17576/jsm-2021-5005-16Search in Google Scholar

Shahi M.S.J.R., Ataharul Islam M. (2016): Survival analysis on phase iii clinical trial of estrogen on stage 3 and 4 prostate cancer patients. International Conference on Analysis of Repeated Measures Data, Dhaka, Bangladesh (1): 1.Search in Google Scholar

Srinivas V., Mehta H., Amin A., Choudary R., Gadgil N., Ravishanker D., Phadke A. (1995): Carcinoma of the prostate state at initial presentation. International Urology and Nephrology 27(4): 419–422.10.1007/BF025500778586514Search in Google Scholar

StataCorp L. P. (2007): Stata data analysis and statistical software. Special Edition Release 10, 733.Search in Google Scholar

StataCorp L.P. (2005): Stata base reference manual. College Station: StataCorp LLC.Search in Google Scholar

Therneau T.M., Lumley, T., (2015): Package ‘survival’. R Top Doc, 128(10), pp.28–33. R package version 3.3-1.Search in Google Scholar

Uddin A.K., Khan Z.J., Johirul Islam A. (2013): Cancer care scenario in Bangladesh. South Asian Journal of Cancer 2(2): 102.10.4103/2278-330X.110510387665924455570Search in Google Scholar

Wickham H., Chang W., Wickham M.H. (2016): Package ‘ggplot2’. Create elegant data visualisations using the grammar of graphics. Version, 2(1), 1-189. R package version 3.4.0.Search in Google Scholar