Competing Risk Analyses of XXXX with @entity454 .

@entity1 @entity204 @entity740 @entity454
INTRODUCTION: @entity740 ( @entity740 ) is an important health problem that gradually leads to @entity454 ( @entity454 ). In @entity454 @entity1 , @entity204 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 @entity454 @entity1 using competing risk analyses. METHODS: In this retrospective longitudinal study, 307 @entity454 @entity1 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 @entity204 (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 @entity204 . Also, the types of dialysis were associated significantly with @entity204 (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 @entity1 who had experienced transplantation survived for the longest survival time. For those for whom receiving the transplantation was considered as @entity204 , 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 @entity204 due to @entity454 , such as renal transplantation, in estimating the survival of these @entity1 might lead to overestimation of the results.
