Rezvaneh Alvandi; Aliakbar Rasekhi; Mehdi Ariana
Volume 21, Issue 4 , 2019, Pages 1-7
Abstract
Background: Cancer is the second leading cause of death globally, and it was responsible for almost 9.6 million deaths in 2018. Breast cancer (BC) is the most common cancer among women with almost two million new cases worldwide in 2018. Thus, it is necessary to study new methods to estimate the survival ...
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Background: Cancer is the second leading cause of death globally, and it was responsible for almost 9.6 million deaths in 2018. Breast cancer (BC) is the most common cancer among women with almost two million new cases worldwide in 2018. Thus, it is necessary to study new methods to estimate the survival predictive factors in BC patients.Objectives: This cohort study aimed to fit a Cox model to BC data using partial likelihood (PL) and new maximum penalized likeli- hood (MPL) methods in order to determine the predictive factors of survival time and compare the accuracy of these two methods.Methods: This prospective cohort study used the data of 356 women with BC registered at the Cancer Research Center of Shahid Beheshti University of Medical Sciences in Tehran, Iran. The patients were identified from 1999 to 2015. The Cox model by new MPL and PL methods was used with variables such as the stage of cancer, tumor grade, estrogen receptor, and several other variables for univariate and multiple analyses.Results: The mean age ± standard deviation (SD) of patients at diagnosis was about 48 ± 11.27 years ranging from 24 to 84 years. Using the new MPL method, in addition to lymphovascular invasion and recurrence variables, estrogen receptor (P = 0.045) also had a statistically significant relationship with survival. The standard errors of most variables were smaller when using the MLP method than the PL method. The overall one-year, two-year, five-year, and 10-year survival rates based on the baseline hazard estimate were 96%, 92%, 70%, and 51%, respectively.Conclusions: In the analysis of BC data, new MPL method can help identify the factors that affect the survival of patients more accurately than usual methods do. This method decreases the standard error of most variables and can be applied for identifying predictive factors more accurately than previous methods.
