OBJECTIVES: 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 consequences of different healthcare interventions. The aim of this article has been twofold: first, to state the benefit, usefulness, and limitations of decision analytic modeling; and second, to propose a list of steps that should be considered when carrying out this sort of analysis in order to increase its credibility and validity for medical decision making. METHODS: After having read the published literature about decision analytic models, a checklist was created, listing the paramount items and points that should always be followed when carrying out disease modeling. RESULTS: A checklist with 3 items and steps to be taken was drawn up to ensure that all important methodologic aspects are considered when performing and assessing decision analytic modeling, thereby constituting a standard guide to good modeling practices. In addition, a set of limitations inherent to the use of decision analytic models is also contemplated, along with a list of recommendations increasing their validity and utility. CONCLUSIONS: Health economic assessments will continue to be an important tool for the correct allocation of resources, and disease analytic modeling will be a potentially invaluable tool in assisting decision makers in this task.
OBJECTIVES: 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 consequences of different healthcare interventions. The aim of this article has been twofold: first, to state the benefit, usefulness, and limitations of decision analytic modeling; and second, to propose a list of steps that should be considered when carrying out this sort of analysis in order to increase its credibility and validity for medical decision making. METHODS: After having read the published literature about decision analytic models, a checklist was created, listing the paramount items and points that should always be followed when carrying out disease modeling. RESULTS: A checklist with 3 items and steps to be taken was drawn up to ensure that all important methodologic aspects are considered when performing and assessing decision analytic modeling, thereby constituting a standard guide to good modeling practices. In addition, a set of limitations inherent to the use of decision analytic models is also contemplated, along with a list of recommendations increasing their validity and utility. CONCLUSIONS: Health economic assessments will continue to be an important tool for the correct allocation of resources, and disease analytic modeling will be a potentially invaluable tool in assisting decision makers in this task.