An Application of the Cox-Aalen Model for Breast Cancer Survival
DOI:
https://doi.org/10.17713/ajs.v35i1.350Abstract
Semiparametric hazard function regression models are among the well studied risk models in survival analysis. The Cox proportional hazards model has been a popular choice in modelling data from epidemiological settings. The Cox-Aalen model is one of the tools for handling the problem of non-proportional effects in the Cox model. We show an application on Piedmont cancer registry data. We initially fit standard Cox model and with the help of the score process we detect the violation of the proportionality assumption. Covariates and risk factors that, on the basis of clinical reasoning, best model baseline hazard are then moved into the additive part of the Cox-Aalen model. Multiplicative effects results are consistent with those of the Cox model whereas only the Cox-Aalen model fully represents the timevarying effect of tumour size.
References
Aalen, O. O. (1980). A Model for Nonparametric Regression Analysis of Counting Processes. In W. Klonecki, A. Kozek, and J. Rosinski (Eds.), Mathematical Statistics and Probability Theory (p. 1-25). New York: Springer Verlag.
Aalen, O. O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8, 907-925.
Aalen, O. O. (1993). Further results on the non-parametric linear regression model in survival analysis. Statistics in Medicine, 12, 1569-1588.
Andersen, P. K., and Gill, R. D. (1982). Cox’s regression model for counting processes: a large sample study. Annals of Statistics, 10, 1100-1120.
Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistics Society, B, 34, 187-220.
Dabrowska, D. M. (1997). Smoothed Cox regression. Annals of Statistics, 25, 1510-1540.
Houwelingen, H. C. (2000). Validation, calibration, revision and combination of prognostic survival models. Statistics in Medicine, 19, 3401-3415.
Huffer, F.W., and McKeague, I.W. (1991). Weighted least squares estimation for Aalen’s additive risk model. Journal of the American Statistical Association, 86, 114-129.
International Union Against Cancer (UICC). (1997). Tnm classification of malignant tumours (5th ed.). New York: Wiley.
Kvaloy, J. T., and Neef, L. R. (2004). Tests for the proportional intensity assumption based on the score process. Lifetime Data Analysis, 10, 139-157.
Lin, D. Y., Wei, L. J., and Ying, Z. (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika, 80, 557-572.
Marzec, L., and Marzec, P. (1997). Generalized martingale-residual process for goodness-of-fit inference in Cox’s type regression models. Annals of Statistics, 25, 683-714.
McKeague, I. W., and Utikal, K. J. (1991). Goodness-of-fit tests for additive hazards and proportional hazards models. Scandinavian Journal of Statistics, 18, 177-195.
Romano, P., Roos, L., and Jollis, J. (1993). Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. Journal of Clinical Epidemiology, 46, 1075-1079.
Sant, M., Aareleid, T., Berrino, F., Lasota, M. B., Carli, P. M., Faivre, J., et al. (2003). Eurocare-3: survival of cancer patients diagnosed 1990-94 – results and commentary.
Annals of Oncology, 14, v61-v118.
Scheike, T. H., and Zhang, M. J. (2002). An additive-multiplicative Cox-Aalen regression model.
Scheike, T. H., and Zhang, M. J. (2003). Extensions and applications of the Cox-Aalen survival model.
Therneau, T. M., and Grambsch, P. M. (2000). New York: Springer.
Wei, L. J. (1984). Testing goodness of fit for proportional hazards model with censored observations. Journal of the American Statistical Association, 79, 649-652.
Zahl, P.-H. (2003). Regression analysis with multiplicative and time-varying additive regression coefficients with examples from breast and colon cancer.
Downloads
Published
How to Cite
Issue
Section
License
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.