title: Markov Influence Diagrams.
abstract: Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs-including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis-with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.
authors.affiliation: Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP).
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Díez
authors.affiliation: Centre for Biomedical Technology, Technical University of Madrid, Spain (MY).
authors.forename: Mar
authors.initials: M
authors.lastname: Yebra
authors.affiliation: School of Health and Related Research, University of Sheffield, UK (IB).
authors.forename: Iñigo
authors.initials: I
authors.lastname: Bermejo
authors.affiliation: Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP).
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Palacios-Alonso
authors.affiliation: Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP).
authors.forename: Manuel Arias
authors.initials: MA
authors.lastname: Calleja
authors.affiliation: Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP).
authors.forename: Manuel
authors.initials: M
authors.lastname: Luque
authors.affiliation: Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP).
authors.forename: Jorge
authors.initials: J
authors.lastname: Pérez-Martín
mesh_terms: D000465:Algorithms; D001296:Audiovisual Aids; D003362:Cost-Benefit Analysis / Q000379:methods*; D003661:Decision Support Techniques; D006304:Health Status; D006801:Humans; D008390:Markov Chains; D008487:Medical History Taking; D015233:Models, Statistical
keywords: Markov models; cost-effectiveness analysis; influence diagrams; outcomes research