Quality of Life (QOL) has gained an increasing concern in today’s patient management practice. However prediction models for QOL in many populations have been provided previously, the sensitivity and specificity analysis of such models are rarely reported. In this study we aimed at determining the sensitivity and specificity of a logistic regression model that predicts QOL in kidney recipients.In a cross sectional study, we evaluated the QOL of 129 kidney transplanted patients, Iran, 2006, by using the SF-36. Employing the binary logistic regression, the related factors of QOL were identified and then, stepwise multiple logistic regression applied to develop a statistical model for prediction of QOL. After defining the model, its sensitivity, specificity, and predictive values were calculated. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model were 50%, 86%, 68%, 74% and 79% respectively. This study reported a high specificity of post-kidney transplantation QOL prediction model, with an acceptable sensitivity. This indicates that some important measures such as QOL are predictable with a high accuracy by simple variables and some statistical methods.