Home About us Editorial board Ahead of print Browse Articles Search Submit article Instructions Subscribe Contacts Login 
  • Users Online: 317
  • Home
  • Print this page
  • Email this page
Year : 2020  |  Volume : 25  |  Issue : 1  |  Page : 38

Parametric survival model to identify the predictors of breast cancer mortality: An accelerated failure time approach

1 Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
2 Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
3 Department of Statistics and Epidemiology, Faculty of Health; Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

Correspondence Address:
Prof. Mohammad Asghari Jafarabadi
Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Golgasht St., Attar e Neshabouri St., Postal Code: 5166614711, Tabriz
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jrms.JRMS_743_19

Rights and Permissions

Background: Breast cancer (BC) was the fifth cause of mortality worldwide in 2015 and second cause of mortality in Iran in 2012. This study aimed to explore factors associated with survival of patients with BC using parametric survival models. Materials and Methods: Data of 1154 patients that diagnosed with BC recorded in the East Azerbaijan population-based cancer registry database between March 2007 and March 2016. The parametric survival model with an accelerated failure time (AFT) approach was used to assess the association between sex, age, grade, and morphology with time to death. Results: A total of 217 (18.8%) individuals experienced death due to BC by the end of the study. Among the fitted parametric survival models including exponential, Weibull, log logistic, and log-normal models, the log-normal model was the best model with the Akaike information criterion = 1441.47 and Bayesian information criterion = 1486.93 where patients with higher ages (time ratio [TR] =0.693; 95% confidence interval [CI] = [0.531, 0.904]) and higher grades (TR = 0.350; 95% CI = [0.201, 0.608]) had significantly lower survival while the lobular carcinoma type of morphology (TR = 1.975; 95% CI = [1.049, 3.720]) had significantly higher survival. Conclusion: Log-normal model showed to be an optimal tool to model the survival of patients with BC in the current study. Age, grade, and morphology showed significant association with time to death in patients with BC using AFT model. This finding could be recommended for planning and health policymaking in patients with BC. However, the impact of the models used for analysis on the significance and magnitude of estimated effects should be acknowledged.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded119    
    Comments [Add]    
    Cited by others 1    

Recommend this journal