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AN ADDITIVE SUBDISTRIBUTION HAZARDS MODEL FOR COMPETING RISKS DATA

*Molydah S Molydah S  -  Department of Mathematics, Faculty of Mathematics and Science, Gadjah Mada University Yogyakarta , Indonesia, 55281, Indonesia
Danardono Danardono  -  Mathematics Master Study Programme, Department of Mathematics, Faculty of Mathematics and Science, Gadjah Mada University Yogyakarta , Indonesia 55281, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Competing risk failure time data occur frequently in medical a number of methods have been proposed for the analysis of these data. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type.  In this paper, we consider a more flexible model for the subdistribution. It is a combination of the additive model and the Cox model and allows one to perform a more detailed study of covariate effects. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy to use. We applied this method to melanoma data and estimated the cumulative death rate for those who died from melanoma after surgical removal of the tumor. It was found that two covariates had a time-varying effect and two other covariates had a constant effect in predicting the cumulative incidence curve in patients who died of melanoma following tumor removal surgery.

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Keywords: Additive Hazard Model; Competing Risk; Cumulative Incidence Function; Subdistribution Hazard
  1. Allison, P. D. (2010). Survival Analysis. United Kingdom: Emerald Group Publishing Ltd
  2. Cox, D. R. (1972). Regression Models and Life-Table. Journal of the Royal Statistical Society: Series B (Methodology), 2(34), 187–220
  3. Collet, D. (2003). Modelling Survival Data in Medical Research (Second Edition). London: Chapman dan Hall/CRS
  4. Danardono, D. (2012), Analisis Data Survival. Yogyakarta: Program Studi Statistika, Universitas Gadjah Mada,
  5. de Wreede, L. C., Fiocco, M., & Putter, H. (2010). The mstate Package for Estimation and Prediction in Non and Semi-parametric Multi-state and Competing Risks Models. Journal of Computer Methods and Programs in Biomedicine, 99, 261–274
  6. Fauzani N. A. A. (2020). Pemodelan Risiko Bersaing dengan Kovariat Bergantung Waktu Menggunakan Hazard Subdistribusi. Theses. Yogyakarta: Magister Matematika, Universitas Gadjah Mada
  7. Fine, J. P. & Gray. R. J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association, 446(94), 496–509
  8. Gray, R. J. (1988). A Class of K-sample Test for Comparing the Cumulative Incidence of a Competing Risk. The Annals of Statistics, 16(3), 1141–1154
  9. Haesook, K.T. (2007). Cumulative Incidence in Competing Risks Data and Competing Risks Regression Analysis. Journal of Clinical Cancer Research, 13(2), 559–565
  10. Lin, D. Y., Wei, L. J., & Ying, Z. (1993). Checking the Cox Model with Cumulative Sums Martingale-Based Residuals. Biometrika, 80(3), 557–572
  11. Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied Survival Analysis: Regression Modeling of Time to Event Data (Second Edition). New York: Wiley
  12. Kleinbaum, D. G. & Klein, M. (2015). Survival Analysis A Self Learning Text (Second Edition), New York: Springer
  13. Klein, P. & Moeschberger, L. (2005). Survival Analysis: Techniques for Censored and Truncated Data (Second Edition). New York: Springer
  14. Kleinbaum, D. G. & Klein, M. (2015). Survival Analysis A Self Learning Text (Third Edition). New York: Springer
  15. Lee. E. T. & Wang. J. W. (2003). Statistical Methods for Survival Data Analysis (Third Edition). New York: Wiley
  16. Martinussen, T. & Scheike, T. H. (2002). A Flexible Additive Multiplicative Hazard Model. Biometrika, 89(2), 283–298
  17. Nugrahaeni, D. K. (2011). Konsep Dasar Epidemiologi. Jakarta: Penerbit Buku Kedokteran EGC
  18. Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Jr., Flournoy, V. T., & Breslow, N. E. (1978). The Analysis of Failure Times in the Presence of Competing Risks. Journal of Biometrics, 4(34), 541–554
  19. Sun, L., Liu, J., Sun, J., & Zhang, M. J. (2006). Modeling The Subdistribution of a Competing Risks. Journal of the Statistica Sinica, 4(16), 1367–1385
  20. Wei, L. J. (1984), Testing Goodness of Fit for Proportional Hazards Model with Censored Observations, Journal of The American Statistical Association, 79(387), 649–652

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