Methods of handling missing values are stated as reported by the authors. All available data for the described outcome measures were extracted at all available selleck chemical timepoints from individual trials. When data were not explicitly stated in the text but given in graphical form, we used calipers to extract data from the appropriate graphs. Data of continuous variables given only in median and/or interquartile range (IQR) were converted to mean and standard deviation

according to methods stated in the Cochrane Handbook.8 Data given only in median, minimum, and maximum were excluded from the analysis. In contrast to kidney transplants, it has been shown that morphological signs of rejection in protocol biopsies of transplanted livers without clinical correlates require no treatment and have no long-term adverse effects.11 Therefore, we only included treated acute rejections in the primary analysis, when the reported acute rejection was stratified into “treated” and “nontreated.” When data on outcome measures were not provided, the authors were contacted to provide more data. We expressed the results of dichotomous outcomes as relative risk (RR) with values of <1 favoring IL-2Ra, and continuous outcomes as weighted mean differences (MD), both with 95% confidence

intervals (CI). We performed the analysis with both random and fixed effects and found no relevant differences. Results reported here used the random effects model, as this is more Selleckchem Maraviroc conservative in the presence of heterogeneity.12 For the random

effects models the amount of residual heterogeneity (i.e., τ2) was estimated by the restricted maximum likelihood (REML) method.13 Confidence intervals for τ2 were obtained by the Q-profile method.14 The model parameters were estimated by way mafosfamide of weighted least squares, with weights equal to the inverse sum of the variance of the estimate and the estimate of the residual heterogeneity. Then Wald-type tests and confidence intervals were obtained for the parameter estimates.13 We analyzed heterogeneity among studies using Cochrane’s Q test and calculating I2 to measure the proportion of total variation due to heterogeneity beyond chance.15 When we observed heterogeneity, we also performed regression diagnostics of random effects models by computing and inspecting the externally Studentized residuals, Cook’s distance, and the weights during the model fitting to identify outlying and/or influential studies.13 Residual heterogeneity was further explored by estimating τ2 and the test statistic Q when each study was removed in turn (leave-one-out deletion).13 We performed subgroup analyses and meta-regression for all primary outcomes and when significant heterogeneity was observed. Subgroups and factors (for meta-regression) defined a priori were methodological quality of trial (i.e.

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