Organ transplantation has long been perceived as a life-saving intervention. However, it is now recognized that the broader objectives include reducing the patient's burden of disease. The components of the burden of disease include (i) mortality, (ii) morbidity, (iii) disability, (iv) psychological distress and (v) resource use. These components may be correlated either positively or negatively, reflecting common disease antecedents or the trade-offs in clinical decisions. Our proposed approach to modeling includes measures of each outcome and, explicitly, their correlations. Its results are the predicted independent and joint probabilities that a patient will experience any specified range of each measure and of combinations of the measures. The methods were tested with data on mortality, morbidity and resource use from patients following kidney transplantation. The predicted probabilities of the measures are consistent with their observed values and distinguish among the prognoses of patients with distinctive risks, such as diabetics. We have shown, and we believe, that it is feasible to assess the interrelated components of the burden of disease in individual patients and hope that our approach may serve as a starting point in the development of a program for inclusion of alternative measures of outcome in decision making in organ transplantation.