SavedAdd your journal

Journals / Cankaya University Journal of Science and Engineering / 2011 / Cilt: 8 - Sayı: 2

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

Nihal ATA
Durdu KARASOY
Pages
1–1
Publication Date
2011-11-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The Cox regression model is the most commonly used regression model for survival data and sensitive to proportional hazards.
📊
Key Findings & Data:In the violation of proportional hazards, several survival models are suggested.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cox regression model · nonproportional hazards · parametric regression models

Abstract

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.

🔖

Cite This Article

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, Cilt: 8 - Sayı: 2, 1–1. https://ajindex.com/articles/746081

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (18)

  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
  9. R.G. Gutierrez, Parametric frailty and shared frailty survival models, The Stata Journal 2 (2002), 22–44
  10. 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
  11. P. Hougaard, Survival models for heterogeneous populations derived from stable distributions, Biometrika 73 (1986), 387–396
  12. 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
  13. E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis, Wiley & Sons, New York 2003
  14. O. O. Aalen,, Modelling heterogeneity in survival analysis by the compound poisson distribution, Annals of Applied Probability 2 (1992), 951–972
  15. D. Collett, Modelling Survival Data in Medical Research, Chapman & Hall, New York 2003
  16. 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
  17. P. Hougaard, A class of multivariate failure time distributions, Biometrika 73 (1986), 671–678
  18. G. A. Whitmore and M. L. T.