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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Competing Risk Analyses of Patients with End-Stage Renal Disease
Reza Tabrizi1, Mahmood Moosazadeh2, Eghbal Sekhavati3
1M.Sc. of Epidemiology, Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Introduction:
Chronic kidney disease (CKD) is an important health problem that gradually leads to end-stage renal disease (ESRD). In ESRD patients, death due to other diseases or some events, such as renal transplantation (known as competing risks), would change the probability of observing the event of interest. The aim of this study was to estimate the survival of ESRD patients using competing risk analyses.
Methods:
In this retrospective longitudinal study, 307 ESRD patients who were older than 20 were recruited from the dialysis and kidney transplant Centers in Kerman City, Iran, from2007 to 2011. To assess the impacts of the investigated factors on the outcome, a cause-specific hazard model and competing risk models were fitted. Also, the cumulative incidence (CI) approach and sensitivity analysis were implemented. All of the analyses were performed using Stata software, V.12.
Results:
The results of competing risk models showed that age and type of dialysis were associated with death (hazard ratio (HR)=1.03, p<0.001 and HR=1.65, p=0.011, respectively). In cause specific hazard model each year increase in age was associated with a 2% increase in the risk of death. Also, the types of dialysis were associated significantly with death (HR=1.93), and the effect of the type of dialysis was estimated as HR=1.51 (p=0.04) when we assumed that all patients who had experienced transplantation survived for the longest survival time. For those for whom receiving the transplantation was considered as death, the HR for the type of dialysis as well as the corresponding p-values were 1.82 and 0.001, respectively.
Conclusion:
Ignoring the competing risks of death due to ESRD, such as renal transplantation, in estimating the survival of these patients might lead to overestimation of the results.
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