Application of a non-parametric non-mixture cure rate model for analyzing the survival of patients with colorectal cancer in Iran
- Author:
Mehdi AZIZMOHAMMAD LOOHA
1
;
Mohamad Amin POURHOSEINGHOLI
;
Maryam NASSERINEJAD
;
Hadis NAJAFIMEHR
;
Mohammad Reza ZALI
Author Information
- Publication Type:Original Article
- From:Epidemiology and Health 2018;40():e2018045-
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES:Colorectal cancer (CRC) patients are considered to have been cured when the mortality rate of individuals with the disease returns to the same level as expected in the general population. This study aimed to assess the impact of various risk factors on the cure fraction of CRC patients using a real dataset of Iranian CRC patients with a non-mixture non-parametric cure model.
METHODS:This study was conducted on the medical records of 512 patients who were definitively diagnosed with CRC at Taleghani Hospital, Tehran, Iran from 2001 to 2007. A non-mixture non-parametric cure rate model was applied to the data after using stepwise selection to identify the risk factors of CRC.
RESULTS:For non-cured cases, the mean survival time was 1,243.83 days (95% confidence interval [CI], 1,174.65 to 1,313.00) and the median survival time was 1,493.00 days (95% CI, 1,398.67 to 1,587.33). The 1- and 3-year survival rates were 92.9% (95% CI, 91.0 to 95.0) and 73.4% (95% CI, 68.0 to 79.0), respectively. Pathologic stage T1 of the primary tumor (estimate=0.58; p=0.013), a poorly differentiated tumor (estimate=1.17; p<0.001), a body mass index (BMI) between 18.6 and 24.9 kg/m2 (estimate=−0.60; p=0.04), and a BMI between 25.0 and 29.9 kg/m2 (estimate=−1.43; p<0.001) had significant impacts on the cure fraction of CRC in the multivariate analysis. The proportion of cured patients was 64.1% (95% CI, 56.7 to 72.4).
CONCLUSIONS:This study found that the pathologic stage of the primary tumor, tumor grade, and BMI were potential risk factors that had an impact on the cure fraction. A non-mixture non-parametric cure rate model provides a flexible framework for accurately determining the impact of risk factors on the long-term survival of patients with CRC.
