Breast cancer survival analysis: Applying the generalized gamma distribution under different conditions of the proportional hazards and accelerated failure time assumptions

• Main Authors: Alireza Abadi, Farzaneh Amanpour, Chris Bajdik, Parvin Yavari
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2012-01-01
• Series: International Journal of Preventive Medicine
• Subjects: Breast cancer; generalized gamma distribution; parametric regression; stratified Cox model; survival analysis
• Online Access: http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2012;volume=3;issue=9;spage=644;epage=651;aulast=Abadi

Background: The goal of this study is to extend the applications of parametric survival models so that they include cases in which accelerated failure time (AFT) assumption is not satisfied, and examine parametric and semiparametric models under different proportional hazards (PH) and AFT assumptions. Methods: The data for 12,531 women diagnosed with breast cancer in British Columbia, Canada, during 1990-1999 were divided into eight groups according to patients′ ages and stage of disease, and each group was assumed to have different AFT and PH assumptions. For parametric models, we fitted the saturated generalized gamma (GG) distribution, and compared this with the conventional AFT model. Using a likelihood ratio statistic, both models were compared to the simpler forms including the Weibull and lognormal. For semiparametric models, either Cox′s PH model or stratified Cox model was fitted according to the PH assumption and tested using Schoenfeld residuals. The GG family was compared to the log-logistic model using Akaike information criterion (AIC) and Baysian information criterion (BIC). Results: When PH and AFT assumptions were satisfied, semiparametric and parametric models both provided valid descriptions of breast cancer patient survival. When PH assumption was not satisfied but AFT condition held, the parametric models performed better than the stratified Cox model. When neither the PH nor the AFT assumptions were met, the log normal distribution provided a reasonable fit. Conclusions: When both the PH and AFT assumptions are satisfied, the parametric and semiparametric models provide complementary information. When PH assumption is not satisfied, the parametric models should be considered, whether the AFT assumption is met or not.