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Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama

Yıl 2011, Cilt: 8 Sayı: 2, - , 01.11.2011

Öz

The Cox regression model is the most commonly used regression model for survival
data and sensitive to proportional hazards. In the violation of proportional hazards,
several survival models are suggested. In this study, frailty model was investigated in case
of nonproportional hazards and a numerical example which includes a data of stomach
cancer patients is done to clarify the model.

Kaynakça

  • [1] T. M. Therneau and P. M. Grambsch,Modelling Survival Data: Extending the Cox Model, Springer, New York 2000.
  • [2] J. O’Quigley and J. Stare, Proportional hazards models with frailties and random effects, Statistics in Medicine 21 (2002), 3219–3233.
  • [3] J. W. Vaupel, K. Manton and E. Stallard, The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16 (1979), 439–454.
  • [4] T. Lancaster, Econometric methods for the duration of unemployment, Econometrica 47 (1979), 939–956.
  • [5] P. K. Andersen, O. Borgan, R. D. Gill and N. Keiding, Statistical Models Based On Counting Processes, Springer Verlag, New York 1993.
  • [6] O. O. Aalen, Effects of frailty in survival analysis, Statistical Methods in Medical Research 3 (1994), 227–243.
  • [7] P. Hougaard, Frailty models for survival data, Lifetime Data Analysis 1 (1995), 255–273.
  • [8] J. P. Klein and M. L. Moeschberger, Multivariate Survival Analysis, Survival Analysis Techniques For Censored And Truncated Data, Springer, New York 1997
  • [9] J. Stare and J. O’Quigley, Fit and frailties in proportional hazards regression, Biometrical Journal 46 (2004), 157–164.
  • [10] R .G. Gutierrez, Parametric frailty and shared frailty survival models, The Stata Journal 2 (2002), 22–44.
  • [11] D. Clayton, A model for association in bivariate life tables and ıts applications in epidemiological studies of familial tendency in chronic disease ıncidence, Biometrika 65 (1978), 141–151.
  • [12] P. Hougaard, Survival models for heterogeneous populations derived from stable distributions, Biometrika 73 (1986), 387–396.
  • [13] A. Pickles, R. Crouchley, E. Simonoff, L. Eaves, J. Meyer, M. Rutter, J. Hewitt and J. Silberg, Survival models for developmental genetic data: age of onset of puberty and antisocial behavior in twins, Genetic Epidemiology 11 (1994), 155-170.
  • [14] E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis, Wiley & Sons, New York 2003.
  • [15] O. O. Aalen,, Modelling heterogeneity in survival analysis by the compound poisson distribution, Annals of Applied Probability 2 (1992), 951–972.
  • [16] D. Collett, Modelling Survival Data in Medical Research, Chapman & Hall, New York 2003.
  • [17] D. Clayton and J. Cuzick, Multivariate generalizations of the proportional hazards model (with discussion), Journal of the Royal Statistical Society, Series A 148 (1985), 82–117.
  • [18] P. Hougaard, A class of multivariate failure time distributions, Biometrika 73 (1986), 671–678.
  • [19] G. A. Whitmore and M. L. T. Lee, A multivariate survival distribution generated by an inverse Gaussian mixture of exponentials, Technometrics 33 (1991), 39–50.
  • [20] S. K. Sahu, D. K. Dey, H. Aslanidou and D. Sinha, A Weibull regression model with gamma frailties for multivariate survival data, Lifetime Data Analysis 3 (1997), 123–137.
  • [21] A. I. Yashin, J. W. Vaupel and I. A. Iachine, Correlated individual frailty: an advantageous approach to survival analysis of bivariate data, Mathematical Population Studies 5 (1995), 145–159.
  • [22] E. Akkaya, Mide Kanseri Verileri Icin Cox Regresyon C¸ ozumlemesi, Rapor, Hacettepe Universitesi Fen Fakultesi Istatistik Bolumu, Ileri Istatistik Projeleri, Ankara 2008.
Yıl 2011, Cilt: 8 Sayı: 2, - , 01.11.2011

Öz

Kaynakça

  • [1] T. M. Therneau and P. M. Grambsch,Modelling Survival Data: Extending the Cox Model, Springer, New York 2000.
  • [2] J. O’Quigley and J. Stare, Proportional hazards models with frailties and random effects, Statistics in Medicine 21 (2002), 3219–3233.
  • [3] J. W. Vaupel, K. Manton and E. Stallard, The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16 (1979), 439–454.
  • [4] T. Lancaster, Econometric methods for the duration of unemployment, Econometrica 47 (1979), 939–956.
  • [5] P. K. Andersen, O. Borgan, R. D. Gill and N. Keiding, Statistical Models Based On Counting Processes, Springer Verlag, New York 1993.
  • [6] O. O. Aalen, Effects of frailty in survival analysis, Statistical Methods in Medical Research 3 (1994), 227–243.
  • [7] P. Hougaard, Frailty models for survival data, Lifetime Data Analysis 1 (1995), 255–273.
  • [8] J. P. Klein and M. L. Moeschberger, Multivariate Survival Analysis, Survival Analysis Techniques For Censored And Truncated Data, Springer, New York 1997
  • [9] J. Stare and J. O’Quigley, Fit and frailties in proportional hazards regression, Biometrical Journal 46 (2004), 157–164.
  • [10] R .G. Gutierrez, Parametric frailty and shared frailty survival models, The Stata Journal 2 (2002), 22–44.
  • [11] D. Clayton, A model for association in bivariate life tables and ıts applications in epidemiological studies of familial tendency in chronic disease ıncidence, Biometrika 65 (1978), 141–151.
  • [12] P. Hougaard, Survival models for heterogeneous populations derived from stable distributions, Biometrika 73 (1986), 387–396.
  • [13] A. Pickles, R. Crouchley, E. Simonoff, L. Eaves, J. Meyer, M. Rutter, J. Hewitt and J. Silberg, Survival models for developmental genetic data: age of onset of puberty and antisocial behavior in twins, Genetic Epidemiology 11 (1994), 155-170.
  • [14] E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis, Wiley & Sons, New York 2003.
  • [15] O. O. Aalen,, Modelling heterogeneity in survival analysis by the compound poisson distribution, Annals of Applied Probability 2 (1992), 951–972.
  • [16] D. Collett, Modelling Survival Data in Medical Research, Chapman & Hall, New York 2003.
  • [17] D. Clayton and J. Cuzick, Multivariate generalizations of the proportional hazards model (with discussion), Journal of the Royal Statistical Society, Series A 148 (1985), 82–117.
  • [18] P. Hougaard, A class of multivariate failure time distributions, Biometrika 73 (1986), 671–678.
  • [19] G. A. Whitmore and M. L. T. Lee, A multivariate survival distribution generated by an inverse Gaussian mixture of exponentials, Technometrics 33 (1991), 39–50.
  • [20] S. K. Sahu, D. K. Dey, H. Aslanidou and D. Sinha, A Weibull regression model with gamma frailties for multivariate survival data, Lifetime Data Analysis 3 (1997), 123–137.
  • [21] A. I. Yashin, J. W. Vaupel and I. A. Iachine, Correlated individual frailty: an advantageous approach to survival analysis of bivariate data, Mathematical Population Studies 5 (1995), 145–159.
  • [22] E. Akkaya, Mide Kanseri Verileri Icin Cox Regresyon C¸ ozumlemesi, Rapor, Hacettepe Universitesi Fen Fakultesi Istatistik Bolumu, Ileri Istatistik Projeleri, Ankara 2008.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

Nihal Ata

Durdu Karasoy Bu kişi benim

Yayımlanma Tarihi 1 Kasım 2011
Yayımlandığı Sayı Yıl 2011 Cilt: 8 Sayı: 2

Kaynak Göster

APA Ata, N., & Karasoy, D. (2011). Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama. Cankaya University Journal of Science and Engineering, 8(2).
AMA Ata N, Karasoy D. Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama. CUJSE. Kasım 2011;8(2).
Chicago Ata, Nihal, ve Durdu Karasoy. “Sağkalım Çözümlemesi için Zayıflık Modeli Ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama”. Cankaya University Journal of Science and Engineering 8, sy. 2 (Kasım 2011).
EndNote Ata N, Karasoy D (01 Kasım 2011) Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama. Cankaya University Journal of Science and Engineering 8 2
IEEE N. Ata ve D. Karasoy, “Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama”, CUJSE, c. 8, sy. 2, 2011.
ISNAD Ata, Nihal - Karasoy, Durdu. “Sağkalım Çözümlemesi için Zayıflık Modeli Ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama”. Cankaya University Journal of Science and Engineering 8/2 (Kasım 2011).
JAMA Ata N, Karasoy D. Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama. CUJSE. 2011;8.
MLA Ata, Nihal ve Durdu Karasoy. “Sağkalım Çözümlemesi için Zayıflık Modeli Ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama”. Cankaya University Journal of Science and Engineering, c. 8, sy. 2, 2011.
Vancouver Ata N, Karasoy D. Sağkalım Çözümlemesi için Zayıflık Modeli ve Mide Kanseri Hastalarına İlişkin Verilerle Bir Uygulama. CUJSE. 2011;8(2).