Buckley-James Model in Survival Analysis

Year 2019, Volume: 7 Issue: 2, 485 - 496, 31.12.2019

Burcu Özcan Durdu Karasoy

https://doi.org/10.17093/alphanumeric.330039

Cited By: 1

Abstract

Cox proportional hazards model is the most common one in survival analysis. Buckley-James model can be offered as an alternative to Cox proportional hazards model. While Buckley-James model is focused on calculation of the expected value of the survival time, Cox proportional hazards model focuses on the relative risk of explanatory variables on the failure event. In this study, Buckley-James model is examined, and is applied on a breast cancer data set. Using this model risk factors affecting survival time of breast cancer is determined.

Keywords

Buckley-James method, survival analysis, Cox proportional hazards model

Yaşam Çözümlemesinde Buckley-James Modeli

Abstract

Yaşam çözümlemesinde en yaygın kullanılan model Cox orantılı tehlikeler modelidir. Cox orantılı tehlikeler modeline alternatif olarak Buckley-James modeli kullanılabilir. Buckley-James modeli yaşam süresinin beklenen değerinin hesaplanmasına odaklı iken Cox orantılı tehlikeler modeli başarısız olaylar üzerinde açıklayıcı değişkenlerin göreli etkilerine odaklıdır. Bu çalışmada, Buckley-James modeli incelenmiş ve meme kanseri verilerine uygulanmıştır. Bu model kullanılarak meme kanserinde yaşam süresini etkileyen risk faktörleri belirlenmiştir.

Keywords

Buckley-James yöntemi, yaşam çözümlemesi, Cox orantılı tehlikeler modeli

References

• Buckley, J. and James, I. (1979). Linear Regression with Censored Data, Biometrika, 66, 3, 429-436.
• Cox, D.R. (1972). Regression Models and Life-Tables, Journal of the Royal Statistical Society, Series B, 34, 2, 187-220.
• Cui, J. (2005). Buckley–James Method for Analyzing Censored Data, with an Aplication to a Cardiovascular Disease and an HIV/AIDS Study, The Stata Journal, 5, 4, 517–526.
• Erdoğan, A., (1993). Orantılı Hazard Modeli, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü Istatistik Anabilim Dalı, Bilim Uzmanlığı Tezi, Ankara.
• Feingold, M. (1993). Choice of Prediction Estimator in Censored Regression Models, Biometrics, 49, 661-664.
• Glasson, S. (2007). Censored Regression Techniques for Credit Scoring, Phd. Thesis In Mathematics And Computing, Rmit University, 25-44.
• Heller, G. and Simonoff, J. S. (1990). A Comparison of Estimators for Regression with a Censored Response Variable, Biometrics, 77, 515-520.
• Heller, G. and Simonoff, J. S., (1992). Proportional Hazards and Linear Regression Models, Biometrics, 48, 1, 101-113.
• Hillis, S. L. (1993). A Comparision of Three Buckley-James Variance Estimators, Communication in Statistics-Simulation and Computation, 22, 4, 955-973.
• James, I. R. and Smith, P. J. (1984). Consistency Results for Linear Regression with Censored Data, The Annals of Statistics, 12, 2, 590-600.
• Jin, Z., Lin, D.Y. and Ying, Z. (2006). On Least Squares Regression with Censored Data, Biometrika, 93, 147-161.
• Kao, C. (1985). Influence Diagnostics for Censored Regression Models, Statistics and Probability Letters, 3, 337-342.
• Lai, T. L. and Ying, Z. (1991). Large Sample Theory of a Modified Buckley-James Estimator for Regression Analysis with Censored Data, The Annals of Statistics, 19, 3, 1370-1402.
• Lin, J. S. and Wei, L. J. (1992). Linear Regression Analysis Based on Buckley James Estimating Equation, Biometrics, 48, 679-681.
• Miller, R. and Halpern, J. (1982). Regression with Censored Data, Biometrika, 69, 3, 521- 531.
• Moon, C. (1989). A Monte Carlo Comparison of Semiparametric Tobit Estimators, Journal of Applied Econometrics, 4, 361–382.
• Ning, J., Qin, J. and Shen, Y. (2011). Buckley-James Type Estimator with Right Censored and Length–Biased Data, Biometrics, 67, 1369-1378.
• Schoenfeld, D. (1982). Partial Residuals for the Proportional Hazards Regression Model, Biometrika, 69, 239-241.
• Smith, P. J. and Zhang, J. (1995). Renovated Scatterplots for Censored Data, Biometrika Trust, 82, 2, 447-452.
• Stare, J., Heinzl, H. and Harrell, F. (2000). On the Use of Buckley and James Least Squares Regression for Survival Data, New Approaches in Applied Statistics, 16, Metodolo ̆Ski Zvezki, Ljubljana: Fdv, 125-134.
• Wang, S., Nan, B., Zhu, J. and Beer, D. G. (2008). Doubly Penalized Buckley-James Method for Survival Data with High-Dimensional Covariates, Biometrics, 64, 132-140.
• Weissfeld, L. A. and Schneider, H. (1987). Inferences based on the Buckley-James procedure, Communications in Statistics, Theory and Methods, 16, 6, 1773-87, (1987).
• Wu, C. P. and Zubovic, Y. (1995). A Large-Scale Monte Carlo Study of the Buckley-James Estimator with Censored Data, Journal of Statistical Computation and Simulation, 51, 97–119.
• Yu, Q. and Wong, G. Y. C. (2002). How to Find all Buckley-James Estimates Instead of Just One?, Journal of Statistical Computation and Simulation, 72, 6, 451-560.
• Yu, M. (2011). Buckley-James Type Estimator for Censored Data with Covariates Missing by Design, Scandinavian Journal of Statistics, 38, 252-267.