Purpose: It is crucial to present results of decision-analytic modeling comprehensibly and clearly in order to obtain trust and acceptance in clinicians and health policy makers. Our first aim was to investigate how outcomes of decision-analytic modeling under uncertainty can be visualized and categorized. The second aim was to give an overview of recommendations of health technology assessment (HTA) agencies and good research practice guidelines for the visualization of decision-analytic studies in the context of HTA.
Method: A systematic evidence table was developed to give an overview of different visualization techniques and categorize them according to their appropriate use. The table was developed using standard textbooks, publications of decision-analytic modeling studies, and in discussion with international modeling experts. Furthermore, a systematic literature review (Medline, Cochrane Library) was performed to elicit guidelines in relation to good research practice in decision analysis and guidelines from HTA organizations were analyzed regarding their recommended visualization techniques.
Result: Our user-friendly evidence table provides a comprehensive overview of the appropriate visualization techniques for several main modeling steps. This table contains two domains: specific analytical approaches (e.g., base-case analysis, first-order Monte Carlo simulation, sensitivity analyses, calibration, validation), and different outcomes (e.g., health outcomes, costs, incremental cost-effectiveness ratio (ICER)). For example, it is recommended to use an efficiency frontier for the visualization of ICERs in a base-case analysis or to use a Markov probability curve for the expected value. The extraction of the guidelines from HTA organizations revealed that they concentrate mainly on visualization of uncertainty. For example, five agencies recommended tornado diagrams to visualize outcomes of one-way sensitivity analyses and six recommended cost-effectiveness acceptability curves (CEAC) to visualize the results of a probabilistic sensitivity analysis. Beyond that, the included good research practice guidelines also recommend the tabular presentation of costs and health outcomes, the Markov probability curve and survival curves in comparison with Kaplan-Meier survival curves from empirical studies to visually enhance external validity.
Conclusion: Most recommendations of HTA organizations focus on the visualization of outcomes of uncertainty and sensitivity analyses and in particular on CEACs. However, this method, as well as economic evaluation in general, often primarily consider the aspects of costs, and omit other considerations, such as ethical issues or the comprehensibility for clinicians and health policy decision makers.
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