What Is the Best Parametric Survival Models for Analyzing Hemodialysis Data?
- Mohsen Vahedi
- Mahmood Mahmoodi
- Kazem Mohammad
- Sharzad Ossareh
- Hojjat Zeraati
BACKGROUND: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are both common public health problems worldwide. Hemodialysis (HD) is one of the main ultimate modalities of renal replacement therapy in these patients. The aim of this study was to compare the different parametric (Weibull, Gamma, Gompertz, Log-logistic and Lognormal) survival models, in maintenance HD (MHD) patients.
METHOD: This study was conducted from March 2004 to October 2013 and encompassed 544 ESRD patients under MHD in Hasheminejad Kidney Center, Tehran, Iran. Laboratory, clinical and demographic data were extracted from the Hemodialysis Data Processor Software, which had been designed for data collection in Hasheminejad Kidney Center. Exponential, Weibull, Gompertz, lognormal and log-logistic were used for analyzing survival of hemodialysis patient using STATA software. To compare these models Akaike Information criterion (AIC) and Cox-Snell residual were utilized.
RESULTS: According to the both criteria (AIC and Cox-Snell residual), Weibull survival model manifested better results as compared with other models. According to this model, age at the time of admission (HR=1.015, p-value=0.018), walking ability (HR=0.656, p-value=0.010), diabetes mellitus as the underlying disease (HR=1.392, p-value=0.038), hemoglobin level (HR=0.790, p-value<0.001), serum creatinine (HR=0.803, p-value<0.001), serum protein (HR=0.747, p-value=0.010) and Single pool Kt/V(HR=0.092, p-value<0.001), had significant effect on survival of the hemodialysis patient.
CONCLUSION: In our analysis Weibull distribution, which had the lowest AIC value, was selected as the most suitable model.
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