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Background. Outcome after liver transplantation (LT) is determined by donor, transplant and recipient risk factors. These factors may have different impact on either patient or graft survival (outcome type). In the literature, there is wide variation in the use of outcome types and points in time (short term or long term). Objective of this study is to analyze the predictive capacity of risk factors and risk models in LT and how they vary over time and per outcome type. Methods. All LTs performed in the Netherlands from January 1, 2002, to December 31, 2011, were analyzed with multivariate analyses at 3-month, 1-year, and 5-year for patient and (non-)death-censored graft survival. The predictive capacity of the investigated risk models was compared with concordance indices. Results. Recipient age, model for end-stage liver disease sodium, ventilatory support, diabetes mellitus, hepatocellular carcinoma, previous malignancy, hepatitis C virus antibody, hepatitis B virus antibody, perfusion fluid, and Eurotransplant donor risk index (ET-DRI) had significant impact on outcome (graft or patient survival) at 1 or multiple points in time. Significant factors at 3-month patient survival (recipient age, model for end-stage liver disease sodium, ventilatory support) were used to compose a concept model. This model, had a higher c-index than the balance-of-risk score, DRI, ET-DRI, donor-recipient model and simplified recipient risk index for long-term patient and non–death-censored graft survival. Conclusions. In this study, the effects of recipient risk factors and models on different outcome types and time points were shown. Short-term patient survival mainly depends on recipient risk factors, long-term graft survival on donor risk factors and is more difficult to predict. Next to the concept model, the donor-recipient model has a higher predictive capacity to other risk models for (long-term) patient and non–death-censored graft survival. The DRI and ET-DRI best predicted death-censored graft survival. Knowledge about risk factors and models is critical when using these for waitlist management and/or help in organ allocation and decision-making.