skip to main content

ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF

*Triastuti Wuryandari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sri Haryatmi Kartiko  -  Departemen Matematika, FMIPA, Universitas Gadjah Mada, Indonesia
Danardono Danardono  -  Departemen Matematika, FMIPA, Universitas Gadjah Mada, Indonesia
Open Access Copyright 2020 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Survival data is the length of time until an event occurs. If  the survival  time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based  on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act  multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of  the additive hazard models is the semiparametric additive  hazard model  that introduced by Lin Ying in 1994.  The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and  method of birth.

 

Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration

Fulltext View|Download
Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration

Article Metrics:

  1. Aalen, O.O., 1980. A model for non-parametric regression analysis of counting process, Mathematical Statistics and Probability Theory
  2. Allison, P.D., 2010. Survival Analysis Using SAS A Practical Guide. Second Edition. 2010. SAS Institude Inc
  3. Aprilia, Y dan Ritchmond, B.L., 2013. Gentlebirth Melahirkan Nyaman Tanpa Rasa Sakit, Penerbit Gramedia Widiasarana Indonesia, Jakarta (2013)
  4. Collett, D., 2003. Modelling Survival Data in Medical Research, Chapman Hall, London
  5. Cox, DR., 1972. Regression Models and Life tables (with discussion). Journal of The Royal Statistical Society: Series B Vol 34:187-220
  6. Huffer, F.W., and McKeague, I.W., 1991. Weighted least squares estimation for Aalen’s additive risk model, Journal American Statistic Association Vol 86 pp. 114-129
  7. Kalbfleisch, J.D. and Prentice, R.L., 2002. The Statistical Analysis of Failure Time Data, Wiley New York
  8. Klein, J.P. and Moeschberger, M.L., 2003. Analysis Techniques for Censored and Truncated Data Second Edition, New York
  9. Madadizadah, F., Ghanbarnejad, A., Ghavani, V., Bandamini, M. Z., 2017., Applying Additive Models for Analysis Survival in Patients with collorectal cancer in Farv Prevince Southerm Iran, Applying AH Models in Colorectal Cancer
  10. McKeague, I.W., Asymptotic Theory for Weighted Least Square Estimators in Aalen’s Additive Risk Model, Contemporary Mathematic Vol 80 (1998)
  11. Sarker, S., Min, D.K, Black, T.R., and Lim, H.J., Application of Multiplicative and Additive Hazards Model to Injury Prevention among Healthcare Workers, Advances in Research ISSN 2348-0394 (2015)
  12. Xie, X., Strickler, H.D. and Xue, X., 2013 Computational and Mathematical Methods in Medicine (2013)

Last update:

No citation recorded.

Last update:

No citation recorded.