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Title: | Predicting the cure rate of breast cancer using a new regression model with four regression structures | Authors: | Ramires, Thiago G. Cordeiro, Gauss M. Kattan, Michael W. HENS, Niel Ortega, Edwin M. M. |
Issue Date: | 2018 | Publisher: | SAGE PUBLICATIONS LTD | Source: | STATISTICAL METHODS IN MEDICAL RESEARCH, 27(11), p. 3207-3223 | Abstract: | Cure fraction models are useful to model lifetime data with long-term survivors. We propose a flexible four-parameter cure rate survival model called the log-sinh Cauchy promotion time model for predicting breast carcinoma survival in women who underwent mastectomy. The model can estimate simultaneously the effects of the explanatory variables on the timing acceleration/deceleration of a given event, the surviving fraction, the heterogeneity, and the possible existence of bimodality in the data. In order to examine the performance of the proposed model, simulations are presented to verify the robust aspects of this flexible class against outlying and influential observations. Furthermore, we determine some diagnostic measures and the one-step approximations of the estimates in the case-deletion model. The new model was implemented in the generalized additive model for location, scale and shape package of the R software, which is presented throughout the paper by way of a brief tutorial on its use. The potential of the new regression model to accurately predict breast carcinoma mortality is illustrated using a real data set. | Notes: | [Ramires, Thiago G.; Ortega, Edwin M. M.] Univ Sao Paulo, Dept Exact Sci, BR-13418900 Piracicaba, Brazil. [Ramires, Thiago G.; Hens, Niel] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat I Biost, Hasselt, Belgium. [Cordeiro, Gauss M.] Univ Fed Pernambuco, Dept Stat, Recife, PE, Brazil. [Kattan, Michael W.] Cleveland Clin, Dept Quantitat Hlth Sci, Desk JJN3-01, Cleveland, OH 44106 USA. [Hens, Niel] Univ Antwerp, Vaccine & Infect Dis Inst, Ctr Hlth Econ Res & Modelling Infect Dis, Antwerp, Belgium. | Keywords: | Cure rate models;regression models;residual analysis;sensitivity analysis;generalized additive model for location;scale and shape | Document URI: | http://hdl.handle.net/1942/28843 | ISSN: | 0962-2802 | e-ISSN: | 1477-0334 | DOI: | 10.1177/0962280217695344 | ISI #: | 000447566100001 | Rights: | The Author(s) 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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