Survival analysis on heart transplantation patients of different ages using Bayesian survival techniques


  • Jishnu Raj Christ University
  • R . Roseline Mary Christ University





Heart Transplant, Survival Analysis, AnovaDDP, Survregbayes.


This study focuses to find the survival chances of the heart transplant patient using different survival analysis. The results are later compared with the survival analysis to check the accuracy. The age-based estimation of survival analysis is also performed. The research carried out using Bayesian Survival analysis with survregbayes, anovaDDP and Kaplan Meir methods for survival analysis. The obtained results have shown that, Bayesian survival analysis is more accurate than the normal survival analysis strategy. Based on variations in mismatched scores, the survival time fluctuates for different age groups. A plot for survregbayes model was generated for which, those below the age of 28 have higher chances of survival. The future enhancement would deploy a PH model for estimation than the currently used PO model. At the later stage the project also aims to find out the cause of hazard from an increased number of variables.





[1] Taylor, B. M., & Rowlingson, B. S. (2017). spatsurv : An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4).

[2] Aitkin, M., Laird, N., Francis, B., & Laird, N. A. N. (2014). A Reanalysis of the Stanford Heart Transplant Data, 78(382), 264–274.

[3] Borkon, A. M., Muehlebach, G. F., Jones, P. G., Bresnahan, D. R., Genton, R. E., Gorton, M. E., … Rowe, S. K. (1999). An analysis of the effect of age on survival after heart transplant. The Journal of Heart and Lung Transplantation: The Official Publication of the International Society for Heart Transplantation, 18(7), 668–674. Retrieved from

[4] Zhou, H., Hanson, T., & Zhang, J. (2016). spBayesSurv: Fitting Bayesian Spatial Survival Models Using R. Journal of Statistical, (Taylor 2017), 18637.

[5] Tsujitani, M., & Tanaka, Y. (2013). Analysis of heart transplant survival data using generalized additive models. Computational & Mathematical Methods in Medicine, 2013, 609857.

[6] Politi, P., Piccinelli, M., Poli, P. F., Klersy, C., Campana, C., Goggi, C., … Barale, F. (2004). Ten years of “extended†life: quality of life among heart transplantation survivors. Transplantation, 78(2), 257–263.

[7] Lee, E. T., & Go, O. T. (1997). Survival analysis in public health research. Annual Review of Public Health, 18, 105–134.

[8] Tjang, Y. S., van der Heijden, G. J. M. G., Tenderich, G., Grobbee, D. E., & Körfer, R. (2008). Survival analysis in heart transplantation: results from an analysis of 1290 cases in a single center. European Journal of Cardio-Thoracic Surgery, 33(5), 856–861.

[9] Kartsonaki, C. (2016). Survival analysis. Diagnostic Histopathology, 22(7), 263–270.

[10] Oztekin, A. (2010). Data Mining Based Survival Analysis and Simulation Modeling for Lung Transplant.

[11] Susarla, V., & Ryzin, J. V. (1976). Nonparametric Bayesian Estimation of Survival Curves from Incomplete Observations. Journal of the American Statistical Association, 71(356), 897–902.

[12] Medved, D., Ohlsson, M., Höglund, P., Andersson, B., Nugues, P., & Nilsson, J. (2018). Improving prediction of heart transplantation outcome using deep learning techniques. Scientific Reports, 8(1), 1–9.

[13] Chang, I. S., Hsiung, C. A., Yuh-Jenn, W. U., & Yang, C. C. (2005). Bayesian survival analysis using Bernstein polynomials. Scandinavian Journal of Statistics.

[14] Gershman, S. J., & Blei, D. M. (2012). A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology, 56(1), 1–12.

[15] Li, Q., Yang, W., Zhu, G. X., & Zhou, J. (2011). Study on survival analysis method for equipment life and its application. 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management, IE and EM 2011, (PART 2), 848–850.

[16] De Iorio M, Johnson WO, M¨uller P, Rosner GL (2009). “Bayesian Nonparametric Nonproportional Hazards Survival Modeling.†Biometrics, 65(3), 762–771.

View Full Article: