Background and Objectives: Decision analysis models are conceptual framework for most of the cost - effectiveness (CEA) and cost-utility (CUA) analyses and this model increasingly plays an important role in decision making. The aim of this study was to improve the understanding and use of decision analysis and economic modeling techniques with a particular emphasis on decision trees and Markov modeling.

Material and Methods: A review of the published literature was performed using the seven search engines and databases which include Web of Science, PubMed, Cochrane, Embase, EconLit, EBSCO and HEED with key words including: Decision Analysis, Health Economic Modeling and TreeAge and their combination to describe the structure, application, and limitations of the more popular decision analytic methods including decision trees, Markov models, and sensitivity analysis in healthcare.

Results: We identified 19 relevant published articles. The results indicated that decision analytical models are widely used in economic evaluation of health care interventions with the objective of generating valuable information to assist health policy decision-makers to allocate scarce health care resources efficiently.

Conclusion: Decision analytic modeling allows a rational, feasible, scientific, and timely approach to measure the efficiency of new medical technologies in health care by using the best available evidence of different sources to produce detailed estimates of the clinical and economic indicators. Despite TreeAge Pro software increasing use in developing countries as economic modeling studies of various health interventions, unfortunately its role and impact are not known in Iran yet.

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