skip to main content

SURVIVAL ANALYSIS FOR RECURRENT EVENT DATA USING COUNTING PROCESS APPROACH: APPLICATION TO DIABETICS

*Triastuti Wuryandari  -  Department of Statistics, Universitas Diponegoro, Indonesia
Yuciana Wilandari scopus  -  Department of Statistics, Universitas Diponegoro, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Survival analysis is a branch of statistics for analyzing the duration of time until one or more events occur. Time to recurrence of diabetics including survival data. Diabetes can’t be cured but it can be controlled. Diabetics who don’t maintain their health and lifestyle will experience recurrence. Factors thought to influence the recurrence of diabetics are internal factors such as genetics and external factors such as lifestyle. The recurrence time of an object includes recurrent events because each object can experience the same recurrent event during the follow-up. One of the analysis to determine factors that are thought to influence the recurrence time of diabetics is survival analysis. Survival data can be modeled into a regression model if the survival time of an object is influenced by other factors. One of the regression models for survival data is Cox regression. One of the Cox regression models for recurrent event data is the AG model which uses a counting process approach. This study used data on the recurrence of diabetics at MH Thamrin Cileungsi Hospital. Based on data analysis, factors that influence the recurrence of diabetics are age, gender, and type of complication.
Fulltext View|Download
Keywords: Survival Analysis; Diabetics; Cox Model; Recurrent Event; AG Model

Article Metrics:

  1. Anandarma, S.O., Asmaningrum, N., & Nur, K.R.M. (2021). Hubungan Efikasi Diri Pasien Diabetes Mellitus Tipe 2 dengan Risiko Rawat Ulang di Rumah Sakit Umum Daerah Dr. Harjono Kabupaten Ponorogo. Jurnal Keperawatan Sriwijaya, 8(2), 39–49
  2. Andersen, P.K., Borgan, O., Gill, R.D., & Keiding, N. (2012). Statistical Models Based on Counting Processes. New York: Springer Science & Business Media
  3. Collett, D. (2015). Modelling Survival Data in Medical Research 3rd ed. New York: Chapman and Hall
  4. Kelly, P.J. & Lim, L. (2000). Survival Analysis For Recurrent Event Data: Application to Childhood Infectious Diseases. Statistics in Medicine, 19, 13–33
  5. Klein, J.P. & Moeschberger, M.L. (2003). Survival Analysis Techniques Truncated Data, 2nd ed. New York: Springer Science
  6. Kleinbaum, D.G. & Klein, M. (2012). Survival Analysis A Self Learning Text, 2nd ed. New York: Springer Science
  7. Lee, E. T. & Wang, J. (2003). Statistical Methods for Survival Data Analysis (Vol. 476). USA: John Wiley & Sons
  8. Lim, H.J. & Zhang, X. (2011). Additive and Multiplicative Hazard Modeling for Recurrent Event Data Analysis. BMC Medical Research Methodology, 11, 1–12
  9. R Core Team. (2021). R: A Language And Environment For Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.r-project.org
  10. Sari, N.W. & Purnami, S.W. (2015). Survival Analysis for Recurrent Event Data with Anderson Gill Approach. Proceedings of the ICONSSE FSM SWC, 51–54
  11. Scaubel, D.E. & Cai, J. (2004). Regression Methods for Gap Time Hazard Functions of Sequentially Ordered Multivariate Failure Time Data. Biometrika, 91. 291–303
  12. Sudarno, S. & Setiani, E. (2019). Hazard Proportional Regression Study to Determine Stroke Risk Factors Using Breslow Method. Media Statistika, 12(2), 200–213
  13. Tampubolon, R & Noeryanti. (2018). Model Regresi Cox pada Data Kejadian Berulang Identik untuk Analisis Penyakit Tuberkulosis Terhadap Pasien Laki-laki. Jurnal Statistika Industri dan Komputasi, 3(2), 33–41
  14. Tsaniya, U., Wuryandari, T., & Ispriyanti, D. 2023. Analisis Survival pada Data Kejadian Berulang Menggunakan Pendekatan Counting Process. Jurnal Gaussian, 11(3), 377–385
  15. Ullah, S., Gabbet, S., & Finch, C.F. (2014). Statistical Modelling for Recurrent Events: an Application to Sports Injuries. BMJ Sport. Br, J. 48(17), 1287–1293
  16. Valliyot, B., Sreedharan, J., Muttappallymyalil, J., & Valliyot, S.B. (2013). Risk Factors of Type 2 Diabetes Mellitus in The Rural Population of North Kerala, India: A Case Control Study. Diabetologia Croatica, 42(1)
  17. Yuhelma, Hasneli, Nauli. (2015). Identifikasi dan Analisis Komplikasi Makrovaskuler dan Mikrovaskuler pada Pasien Diabetes Mellitus. Jurnal Online Mahasiswa Program Studi Ilmu Keperawatan Universitas Riau, 2(1), 569–579

Last update:

No citation recorded.

Last update: 2024-11-02 23:59:33

No citation recorded.