Many pharmacogenomic predictors of drug response are now available, and include both drug metabolism-disposition factors and drug targets. Information on statistical approaches to analyzing large clinical data sets in relation to genetic polymorphisms is limited. The objective of this study was to evaluate whether logistic regression could identify pharmacogenomic predictors of outcome in a large data set in a complex transplant patient population. Seventy pediatric heart transplant patients were studied. Patients were followed for at least 1 yr post-transplantation as outpatients, and weaned from corticosteroids if clinically appropriate. Logistic regression analysis was used to identify the predictors of steroid dependency. The dependent variable was the presence or absence of steroid therapy at 1 yr post-transplantation. The independent variables were the patients' transplant age, gender, MDR1 C3435T and G2677T, CYP3A53B and cytokine polymorphisms. By chi-square test for the MDR1 C3435T polymorphism, 12 of 18 (67%) patients in the CC group were still on prednisone, whereas only 18 of 47 (38%) of the CT/TT group were still receiving prednisone (p = 0.04). For the IL-10 groups, two of 15 patients with the high producer genotype (13.3%) remained on prednisone, in comparison with 16 of 28 patients with the intermediate producer genotype (57.1%) and 15 of 26 patients with the low producer genotype (57.7%, p = 0.01). Logistic regression analysis confirmed MDR1 C3435T (p = 0.021), and IL-10 polymorphisms (intermediate producer genotype p = 0.015; low producer genotype p = 0.013) as independent risk factors for steroid dependency at 1 yr after transplantation. This approach identifies pharmacogenomic factors, which can be studied more extensively in larger data sets, and used in prospective studies to individualize immunosuppressive therapy following solid organ transplantation.