Survival Analysis of Gastric Cancer Patients with Incomplete Data.
10.5230/jgc.2014.14.4.259
- Author:
Abbas MOGHIMBEIGI
1
;
Lily TAPAK
;
Ghodaratolla ROSHANAEI
;
Hossein MAHJUB
Author Information
1. Modeling of Noncommunicable Disease Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. moghimbeigi@umsha.ac.ir
- Publication Type:Original Article
- Keywords:
Survival analysis;
Hazard Model;
Stomach neoplasms;
Regression
- MeSH:
Diagnosis;
Drug Therapy;
Humans;
Markov Chains;
Proportional Hazards Models;
Stomach Neoplasms*;
Survival Analysis*
- From:Journal of Gastric Cancer
2014;14(4):259-265
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. MATERIALS AND METHODS: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. RESULTS: The mean patient survival time after diagnosis was 49.1+/-4.4 months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). CONCLUSIONS: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.
