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Influence diagrams are ideal knowledge representations for Bayesianstatistical models. However, these diagrams are difficult for end users tointerpret and to manipulate. We present a user-based architecture that enablesend users to create and to manipulate the knowledge representation. We use theproblem of physicians' interpretation of two-arm parallel randomized clinicaltrials (TAPRCT) to illustrate the architecture and its use. There are threeprimary data structures. Elements of statistical models are encoded assubgraphs of a restricted class of influence diagram. The interpretations ofthose elements are mapped into users' language in a domain-specific, user-basedsemantic interface, called a patient-flow diagram, in the TAPRCT problem.Pennitted transformations of the statistical model that maintain the semanticrelationships of the model are encoded in a metadata-state diagram, called thecohort-state diagram, in the TAPRCT problem. The algorithm that runs the systemuses modular actions called construction steps. This framework has beenimplemented in a system called THOMAS, that allows physicians to interpret thedata reported from a TAPRCT.

End-User Construction of Influence Diagrams for Bayesian Statistics