Abstract
BackgroundIn the construction of pharmacoeoconomic models, simplicity is desirable for transparency (people can see how the model is built), ease of analysis, validation (how well the model reproduces reality), and description. Few reports have described concrete methods for constructing simpler models. Therefore we focused on the value of additional states and uncertainty in disease models with multiple complications.ObjectivesThe objective of this study was to examine the possibility of ranking additional states in disease models with multiple complications using a method for evaluating the quantification and uncertainty of additional states.MethodsThe expected value of additional states (EVAS) was formulated to calculate the value of additional states from the variation between analytic models using the net benefit method, and uncertainty was subtracted from the variation. We also verified the usefulness and availability of this method in grade I hypertension as a verification of the disease model. We assumed that stroke was recognized as an associated complication of hypertension in the basic model. In addition, stroke recurrence, coronary heart disease (CHD), and end-stage renal disease (ESRD) were assumed to represent other complications of hypertension. Ten thousand Monte Carlo simulations were performed, and the probability distribution was assumed to be the beta distribution in clinical parameters. The ranges of clinical parameters were ±6.25%, 12.5%, 25%, and 50% of the standard deviation from the mean value.ResultsThe EVAS in complications of CHD showed the greatest uncertainty. In contrast, the EVAS of ESRD differed from stroke recurrence in the value ranking by uncertainty.ConclusionsThe EVAS has the potential to determine the ranking of additional states based on the quantitative value and uncertainty in disease models with multiple complications.Electronic supplementary materialThe online version of this article (doi:10.1186/s40780-014-0006-z) contains supplementary material, which is available to authorized users.
Highlights
In the construction of pharmacoeoconomic models, simplicity is desirable for transparency, ease of analysis, validation, and description
The expected value of additional states (EVAS) has the potential to determine the ranking of additional states based on the quantitative value and uncertainty in disease models with multiple complications
The objective of this study was to examine the possibility of ranking additional states in disease models with multiple complications using a method for evaluating the quantification and uncertainty of additional states
Summary
In the construction of pharmacoeoconomic models, simplicity is desirable for transparency (people can see how the model is built), ease of analysis, validation (how well the model reproduces reality), and description. We focused on the value of additional states and uncertainty in disease models with multiple complications. In the construction of analytical (disease) models, we assume that multiple complications may occur as the outcome of a specific disease. The complications in the analytical model are selected based on their importance (generally referred to as “a state”), but currently there is no evaluation method for that purpose. If we could attempt to estimate the quantitative value of additional states in an analytical model and examine the possibility of ranking additional states in disease models with multiple complications, it would contribute to the establishment of useful analytical models. In this study we attempted to estimate the quantitative value of additional states and rank those additional states, in addition to evaluating the robustness of ranking while considering uncertainty
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