Decision-analytic modelling
While studies are being performed to inform decision makers about optimal DI tests, these studies often focus on the accuracy and short-term effects of the imaging test. However, for decision making it is also important to know how well a test can help to improve health outcome (e.g., survival and/or quality of life) [16, 21]. In addition, the societal impact may be relevant, including the cost-effectiveness or value for the money needed for a diagnostic test. It is possible to undertake trials that include such long-term consequences but these trials are often costly and practically challenging, due to the remoteness of the effects, requiring long follow-up periods and large sample sizes. Moreover, withholding a non-invasive imaging test that might provide useful diagnostic information may create ethical dilemmas.
Given these limitations, decision-analytic modelling is often used to synthesize data from trials with other available evidence [13]. Hence, the characteristics of a diagnostic test (e.g., sensitivity and specificity) can be linked with long-term patient outcomes. The results of these models tend to better fit the needs of decision makers.
The development and analysis of a decision-analytic model proceeds in a stepwise fashion. Comprehensive guidelines were recently published [22]. We present a six-step methodology for CEA in DI, including: (1) Defining the decision problem, (2) Choosing, and further developing the decision model, (3) Selecting input parameters, (4) Analysis and uncertainty analysis, (5) Interpretation of results , and (6) Transferability and validation.