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In this analysis, death and graft failure were treated as competing risks, while patients still alive at the end of follow-up with a functioning graft were censored. To control for confounders related to recipient race that may influence observed differences in DRI, a 1:1 propensity-matched (23) cohort of African American and Caucasian recipients was created using a nearest neighbor algorithm (24). Matching was without replacement. Propensity scores were developed through logistic regression, http://www.selleck.cn/products/azd9291.html with immunologic (HLA-A, -B and -DR antigens, peak PRA and ABO antigen), socioeconomic (payment source, educational achievement, employment status and UNOS Region) and medical (age, recipient bmi, etiology of renal failure, recipient diabetes status, weight, gender, recipient HCV status, recipient CMV status and days on the waiting list) factors as independent variables and recipient race as the dependent variable. The result of http://www.selleckchem.com/products/Gemcitabine-Hydrochloride(Gemzar).html this model is to assign each patient a propensity score that describes the probability of the patient either being African American or Caucasian based upon the independent variables entered in the model. Differences in the overall DRI as well as individual components were then assessed in the propensity-matched cohort. To determine the relative contribution of social versus medical/immunologic confounders, separate propensity-matched cohorts were also created based on the social and medical/immunologic covariates listed above. To explore the contribution of DRI to disparities in graft survival between African Americans and Caucasians, several http://www.selleckchem.com/products/birinapant-tl32711.html Cox regression models were examined in sequence. First univariable models were fitted with recipient race and DRI respectively as the sole predictor variables to determine the baseline unadjusted hazard for graft failure posed by these factors. Next, to control for other potentially relevant covariates, a multivariable Cox regression model was fitted using purposeful variable selection (25,26). A full model was initially fitted with all the covariates used to estimate the propensity score listed above. Those factors which were either significant at the p