A transparent description of all inputs and choices is also important for reasons of validity. In every decision-analytic model, the validity of the results is dependent on the validity of the inputs. A model can be validated by means of face validity (evaluation of model structure, data sources, assumptions and results by experts), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events) [42].