Prognostic Factors of Breast Cancer Survival in the Islamic Republic of Iran: an Additive Empirical Bayesian Approach

Breast cancer is a public health challenge in the Islamic Republic of Iran. Relatively few studies have dealt with large dataset and advanced statistical methods. Therefore, we aimed to investigate the role of prognostic factors on breast cancer survival using Additive Empirical Bayesian model with large data set. Breast cancer data set included 1574 women diagnosed with breast cancer from 2002 to 2012 that registered from Cancer Registry in Fars Province, Islamic Republic of Iran. Overall survival rates at 2, 3, 5 and 10 years were 0.98, 0.94, 0.87 and 0.76, respectively. Five years survival at stages 1, 2 and 3 were 0.94, 0.92 and 0.74, respectively. The younger patients with characteristics such as zero involved nodes, negative progesterone receptor, free skin and good prognostic level had a higher survival chance than others. The 5-year survival probability by stage in Fars Province was nearly the same as that reported by the American Cancer Society. The Nottingham prognostic index [NPI] related to nodal status, tumour size and nuclear grade was the main indicator of breast cancer mortality

A comparison of breast cancer survival among young, middle-aged, and elderly patients in southern Iran using Cox and empirical Bayesian additive hazard models / 한국역학회지

OBJECTIVES: A survival analysis of breast cancer patients in southern Iran according to age has yet to be conducted. This study aimed to quantify the factors contributing to a poor prognosis, using Cox and empirical Bayesian additive hazard (EBAH) models, among young (20-39 years), middle-aged (40-64 years), and elderly (≥ 65 years) women. METHODS: Data from 1,574 breast cancer patients diagnosed from 2002 to 2012 in the cancer registry of Fars Province (southern Iran) were stratified into 3 age groups. The Kaplan-Meier method was used to estimate the overall survival rates. Cox and EBAH models were applied to each age category, and the Akaike information criterion was used to assess the goodness-of-fit of the 2 hazard models. RESULTS: As of December 2012, 212 women (13.5%) in our study population had died, of whom 43 were young (15.3%), 134 middle-aged (11.8%), and 35 elderly (22.3%). The 5-year survival probability by age category was 0.83 (standard error [SE], 0.03), 0.88 (SE, 0.01), and 0.75 (SE, 0.04), respectively. CONCLUSIONS: The Nottingham Prognostic Index was the most effective prognostic factor. The model based on Bayesian methodology performed better with various sample sizes than the Cox model, which is the most widely used method of survival analysis.

A comparison of breast cancer survival among young, middle-aged, and elderly patients in southern Iran using Cox and empirical Bayesian additive hazard models / 한국역학회지

OBJECTIVES: A survival analysis of breast cancer patients in southern Iran according to age has yet to be conducted. This study aimed to quantify the factors contributing to a poor prognosis, using Cox and empirical Bayesian additive hazard (EBAH) models, among young (20-39 years), middle-aged (40-64 years), and elderly (≥ 65 years) women.METHODS: Data from 1,574 breast cancer patients diagnosed from 2002 to 2012 in the cancer registry of Fars Province (southern Iran) were stratified into 3 age groups. The Kaplan-Meier method was used to estimate the overall survival rates. Cox and EBAH models were applied to each age category, and the Akaike information criterion was used to assess the goodness-of-fit of the 2 hazard models.RESULTS: As of December 2012, 212 women (13.5%) in our study population had died, of whom 43 were young (15.3%), 134 middle-aged (11.8%), and 35 elderly (22.3%). The 5-year survival probability by age category was 0.83 (standard error [SE], 0.03), 0.88 (SE, 0.01), and 0.75 (SE, 0.04), respectively.CONCLUSIONS: The Nottingham Prognostic Index was the most effective prognostic factor. The model based on Bayesian methodology performed better with various sample sizes than the Cox model, which is the most widely used method of survival analysis.