  Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicians' interpretation of two-arm parallel randomized clinical
trials (TAPRCT) to illustrate the architecture and its use. There are three
primary data structures. Elements of statistical models are encoded as
subgraphs of a restricted class of influence diagram. The interpretations of
those elements are mapped into users' language in a domain-specific, user-based
semantic interface, called a patient-flow diagram, in the TAPRCT problem.
Pennitted transformations of the statistical model that maintain the semantic
relationships of the model are encoded in a metadata-state diagram, called the
cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system
uses modular actions called construction steps. This framework has been
implemented in a system called THOMAS, that allows physicians to interpret the
data reported from a TAPRCT.
