- Volume 16 Issue 15
We used to LASSO-Cox method for determining prognostic factors of male breast cancer survival and showed the superiority of this method compared to Cox proportional hazard model in low sample size setting. In order to identify and estimate exactly the relative hazard of the most important factors effective for the survival duration of male breast cancer, the LASSO-Cox method has been used. Our data includes the information of male breast cancer patients in Fars province, south of Iran, from 1989 to 2008. Cox proportional hazard and LASSO-Cox models were fitted for 20 classified variables. To reduce the impact of missing data, the multiple imputation method was used 20 times through the Markov chain Mont Carlo method and the results were combined with Rubin's rules. In 50 patients, the age at diagnosis was 59.6 (SD=12.8) years with a minimum of 34 and maximum of 84 years and the mean of survival time was 62 months. Three, 5 and 10 year survival were 92%, 77% and 26%, respectively. Using the LASSO-Cox method led to eliminating 8 low effect variables and also decreased the standard error by 2.5 to 7 times. The relative efficiency of LASSO-Cox method compared with the Cox proportional hazard method was calculated as 22.39. The19 years follow of male breast cancer patients show that the age, having a history of alcohol use, nipple discharge, laterality, histological grade and duration of symptoms were the most important variables that have played an effective role in the patient's survival. In such situations, estimating the coefficients by LASSO-Cox method will be more efficient than the Cox's proportional hazard method.
Cox proportional hazard;high dimension;LASSO;low sample size;male breast cancer survival
- Casella G, Berger RL (1990). Statistical inference, Duxbury Press Belmont, CA.
- Cox DR (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 187-220.
- Craven P, Wahba G (1978). Smoothing noisy data with spline functions. Numer Math, 31, 377-403. https://doi.org/10.1007/BF01404567
- Fentiman IS, Fourquet A, Hortobagyi GN (2006). Male breast cancer. Lancet, 367, 595-604. https://doi.org/10.1016/S0140-6736(06)68226-3
- Goeman JJ (2010). L1 penalized estimation in the cox proportional hazards model. Biometrical J, 52, 70-84.
- Gui J, Li H (2005). Penalized Cox regression analysis in the highdimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics, 21, 3001-8. https://doi.org/10.1093/bioinformatics/bti422
- Kuroi K, Toi M (2003). Male breast cancer]. Gan to kagaku ryoho. Cancer Chemotherapy, 30, 599.
- Miao H, Verkooijen HM, Chia K-S, et al (2011). Incidence and outcome of male breast cancer: an international populationbased study. J Clin Oncol, 29, 4381-6. https://doi.org/10.1200/JCO.2011.36.8902
- Rubin DB (1977). Formalizing subjective notions about the effect of nonrespondents in sample surveys. J Am Stat Associat, 72, 538-43. https://doi.org/10.1080/01621459.1977.10480610
- Salehi A, Zeraati H, Mohammad K, et al (2011). Survival of male breast cancer in Fars, South of Iran. Iranian Red Crescent Med J, 13, 99.
- Soliman AA, Denewer AT, El-Sadda W, et al (2014). A retrospective analysis of survival and prognostic factors of male breast cancer from a single center. BMC cancer, 14, 227. https://doi.org/10.1186/1471-2407-14-227
- Tibshirani R (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267-88.
- Tibshirani R (1997). The lasso method for variable selection in the cox model. Stat Med, 16, 385-95. https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
- Zare N, Doostfatemeh M, Rezaianzadeh A (2012). Modeling of breast cancer prognostic factors using a parametric loglogistic model in fars province, Southern Iran. Asian Pac J Cancer Prev, 13, 1533-7. https://doi.org/10.7314/APJCP.2012.13.4.1533
- Zare N, Haem E, Lankarani KB, et al (2013). Breast cancer risk factors in a defined population: weighted logistic regression approach for rare events. J Breast Cancer, 16, 214-9. https://doi.org/10.4048/jbc.2013.16.2.214