Reference: Lehmann, H. P. A Decision-Theoretic Model for Using Scientific Data. Elsevier Science Publishers B.V., North-Holland, 1990.
Abstract: Many Artificial Intelligence systems depend on the agent's updating its beliefs about the world on the basis of experience. Experiments constitute one type of experience, so scientific methodology offers a natural environment for examining the issues attendant to using this class of evidence. We present a framework which structures the process of using scientific data from research reports for the purpose of making decisions, using decision analysis as the basis for the structure, and using medical research as the general scientific domain. The structure extends the basic influence diagram for updating belief in an object domain parameter of interest by expanding the parameter into four: the patients, the populations, the study samples, and the effective study samples. The structure uses biases to perform the transformation of one parameter into another, so that, for instance, selection biases, in concert with the population parameter, yield the study sample parameter. We are able to incorporate over 20 biases catalogued by Sackett (1979), using mathematical formulations offered by Shachter and colleagues (1989), with basic semantics suggested by Feinstein (1985). The influence diagram structure provides decision theoretic justification for practices of good clinical research, such as randomized assignment and blindfolding of care providers. The model covers most research designs used in medicine: case-control studies, cohort studies, and controlled clinical trials. The proposed general model can be the basis for clinical epidemiological advisory systems, when coupled with heuristic pruning of irrelevant biases; of statistical workstations, when the computational machinery for calculation of posterior distributions is added; and of metaanalytic reviews, when multiple studies may impact on a single population parameter.