Volume 8, Number 2 (20 2012)                   irje 2012, 8(2): 29-38 | Back to browse issues page

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Akhlaghi A, Hosseini M, Mahmoodi M, Shamsipour M, Najafi E. A Comparison Between Weibull, Gama, Log -Normal and Log -Logistic Mixture Cure Models in Survival Analysis of Patients Undergoing (Continuous Ambulatory Peritoneal Dialysis) CAPD. irje. 2012; 8 (2) :29-38
URL: http://irje.tums.ac.ir/article-1-6-en.html

1- , mhossein110@yahoo.com
Abstract:   (9281 Views)
Background & Objectives: Peritoneal dialysis is one of the most common types of dialysis in patients with renal failure. However multivariate analysis such as log- rank test and Cox have usually used to evaluate association of risk factors in survival of this group of patients, the aim of this study was to perform of Weibull, Gamma, Lognormal and Logistic Mixture cure models in survival analysis of these patients.
Methods: Data of 433 patients undergoing CAPD who registered in two centers in Tehran, Iran between 1997 to 2009 were used in this analysis. We investigated center, gender, age, cholesterol, Low Density Lipoprotein (LDL), High density lipoprotein (HDL), triglyceride, albumin, hemoglobin, creatinine, Fasting Blood Sugar (FBS), calcium and phosphorous as variables effect with Kaplan-Meier and cure model. CUREREGR module was used for survival analysis.
Results: Comparison of AIC (Akaike Information Criterion) of Weibull, Gama, Lognormal and Logistic Mixture cure models showed that Weibull distribution AIC is lower for almost all variables than other distributions. Weibull distribution has better fitness for data than others. In the multivariate Weibull model, age and albumin variables had significant effect on long-term survival of patients (P<0.01). Triglycerides effect on long-term survival had borderline (P = 0.065). Also HDL, FBS and calcium were significant on short term survival (P<0.01) but significance of LDL was borderline (P=0.088).
Conclusion: Cure models have the ability to analyze dialysis patients' survival data and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate and would help to make better prediction for patients by health care professionals.

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Type of Study: Research | Subject: General
Received: 2011/08/21 | Accepted: 2011/10/29 | Published: 2013/08/18

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