Development and Use of Disease-Specific (Reference) Models for Economic Evaluations of Health Technologies: An Overview of Key Issues and Potential Solutions

Decision-analytic models are increasingly used to inform decisions about whether or not to publicly fund new health technologies such as pharmaceuticals. Therefore, few would argue the need for developing and using high-quality models [1]. Over the past years, significant efforts have been made to improve the quality of decision-analytic models (e.g., through improvement and use of good practice guidelines [2]); however, important challenges facing decision-analytic modelling still remain. Recently, Caro and Moller [3] outlined some of these challenges, including, among other things, ‘‘validation’’, ‘‘transparency’’, ‘‘uncertainty’’, and ‘‘implementing the model’’, with potential impact on the credibility of model outcomes to decision makers. Of these critical points, uncertainty of model outcomes is a broadly studied topic. Three major types of uncertainty influencing the results of decision-analytic models are (1) parameter uncertainty; (2) methodological uncertainty; and (3) structural uncertainty [4]. The impact of both parameter uncertainty (uncertainty in numerical values assigned to model inputs such as transition probabilities) and methodological uncertainty (uncertainty in the choice of analytical methods, e.g., discount rate or the perspective taken) has been addressed substantially in the literature through, for example, using probabilistic sensitivity analyses to address parameter uncertainty [5] and using a ‘reference case’ to deal with methodological uncertainty [6–8]. In contrast, in the absence of a sufficient level of detail around the effect of and approaches to dealing with structural uncertainty, concerns regarding the credibility of model outcomes are frequently raised [9]. Structural uncertainty arises because the model is a simplification of reality and the question is whether alternative assumptions in the model regarding, for instance, the structure do not unequivocally fit reality better than another structure. Recent research has indicated the large impact of this structural uncertainty on outcomes of economic evaluations [10, 11]. Different structures incorporated in, for instance, breast cancer models had a large impact on the differences in incremental cost-effectiveness ratios (ICERs). Several of the implemented structures even incorrectly reflected the natural history of breast cancer progression; therefore, these could even not be seen as structural uncertainty but as biased structures. These findings show the effect of structural uncertainty on model outcomes and, hence, on funding decisions. To reduce the impact of varying model structures on outcomes for a specific disease, the use of standardized disease-specific (reference) models has been recently proposed [10, 12–14]. In 1996, Gold et al. [6] introduced the use of a reference case by describing a set of core requirements such as the need for discounting, sensitivity analyses, and time & Gerardus W. J. Frederix G.W.J.Frederix@uu.nl