Parametric methods for estimating survival in continuous ambulatory peritoneal dialysis patients in the presence of competing events
Background & Aim: In many studies, the survival of patients with chronic kidney disease who are treated with peritoneal dialysis technique has been considered, while this is possible in peritoneal dialysis patients to switch to another treatment. To achieve more accurate estimation of patient survival is necessary to examine all events. The purpose of this study is to estimate the cumulative incidence function (CIF) of events using competing risks method and then calculating the survival of patients treated with peritoneal dialysis.
Methods & Materials: This study includes 417 patients with chronic kidney disease who were under peritoneal dialysis between July 1996 and December 2009 in three centers in Tehran. We achieved their survival by 13 years follow-up time. We have collected patient demographic data and clinical characteristics. CIF of death and other events was estimated using the cause-specific hazard approach and direct approach. Parametric regression model was used to adjust the effects of covariates. The data analysis was performed using the R software.
Results: In this study, the median follow-up time was 664 days. A total of 112 (26.9%) patients treated with peritoneal dialysis died before completing the study, and before the end of the study. One hundred sixty seven (40.0%) patients treated with peritoneal dialysis changed their dialysis method to hemodialysis or had renal transplantation .
Conclusion: The effective risk factors on death CIF and other competing events CIF were diabetes mellitus, albumin, creatinine, diastolic blood pressure, urea and age, creatinine, diastolic blood pressure, respectively.
Burton DR. Uptodate edition 15.1 [Online]. [cited 2007]; Available from: URL http://www.uptodate.com.
Evans DW, Ryckelynck JP, Fabre E, Verger C. Peritonitis-free survival in peritoneal dialysis: an update taking competing risks into account. Nephrol Dial Transplant 2010;25(7): 2315-22.
Pintilie M. Competing risks: a practical perspective. Hoboken, NJ: John Wiley & Sons; 2006.
Jeong JH, Fine J. Direct parametric inference for the cumulative incidence function. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2006; 55(2):187-200.
Jeong JH, Fine JP. Parametric regression on cumulative incidence function. Biostatistics 2007; 8(2): 184-96.
Chung SH, Lindholm B, Lee HB. Is malnutrition an independent predictor of mortality in peritoneal dialysis patients? Nephrol Dial Transplant 2003; 18(10):2134-40.
Johnson DW, Wiggins KJ, Armstrong KA, Campbell SB, Isbel NM, Hawley CM. Elevated white cell count at commencement of peritoneal dialysis predicts overall and cardiac mortality. Kidney Int 2005; 67(2):738-43
Einwohner R, Bernardini J, Fried L, Piraino B. The effect of depressive symptoms on survival in peritoneal dialysis patients. Perit Dial Int 2004; 24(3): 256-63.
Noh H, Lee SW, Kang SW, Shin SK, Choi KH, Lee HY, et al. Serum C-reactive protein: a predictor of mortality in continuous ambulatory peritoneal dialysis patients. Perit Dial Int 1998; 18(4): 387-94.
|Issue||Vol 1 No 1/2 (2015)|
|dialysis cumulative incidence function cause-specific hazard approach Gomperz distribution|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|