Formula Pharma by Mark JC Nuijten.

work info

Articles 'Methods, policy & innovation'

In search for more confidence in outcomes of health economic models.

In this paper published in top-journal Medical Decision Making, we presented an original and innovative approach to handling uncertainty in health economic models.

The paper was based on collaboration with the wide acclaimed University of Technology in Delft, The Netherlands. read more

Objective: Sensitivity analyses are often performed on only a limited number of variables without justification of the choice of variables and range of each variable. External parties such as health authorities are increasingly requiring submission of the actual model, often in order to test the robustness of the outcomes of the model by performing additional sensitivity analyses.

The objective of this work was to develop an alternative method to capture the critical issues of a sensitivity analysis in a health economic model, especially regarding the selection of variables and determining the range for each variable. Apart from external parties such as health authorities, journal readers who want to perform their own sensitivity analysis but do not have access to the model may find this useful. Methods and Results: Statistical methods based on Markov chain modeling and

regression analysis, using the framework of the Taylor series expansion around a point, are used to derive an equation for 1-way sensitivity analyses. In particular, equations for costs and effects are being developed, from which the cost-effectiveness ratio is built. The article shows the feasibility of such equations for the execution of 1-way sensitivity analyses. Conclusion: An equation that can be derived in the manner described in this article provides a substantial amount of information. The inclusion of such an equation in a report may increase transparency of the reporting of outcomes of health economic models.

Cost-effectiveness models & Budget impact models

Budget impact analysis requires a Chrystal ball.

This state-of-the art paper provides you with essential guidance for performance of a budget impact analysis. The key drivers are the forecasts, like growth, uptakes curves, and substitution effects.

Our approach has been successfully applied in market access submission to health authorities and allowed to put drug costs in broader perspective beyond the silo-mentality read more

The objective of this paper was to address the importance of dealing systematically and comprehensively with uncertainty in a budget impact analysis (BIA) in more detail. The handling of uncertainty in health economics was used as a point of reference for addressing the uncertainty in a BIA. This overview shows that standard methods of sensitivity analysis, which are used for standard data set in a health economic model (clinical probabilities, treatment patterns, resource utilisation and prices/tariffs), cannot always be used for the input data for the BIA model beyond the health economic data set for various reasons. Whereas in a health economic model, only limited data may come from a Delphi panel, a BIA model often relies on a majority of data taken from a Delphi panel. In addition, the dataset in a BIA model also includes forecasts (e.g. annual growth, uptakes curves, substitution effects, changes in prescription restrictions and guidelines, future distribution of the available treatment modalities, off-label use). As a consequence, the use of standard sensitivity analyses for BIA data set might be limited because of the lack of appropriate distributions as data sources are limited, or because of the need for forecasting. Therefore, scenario analyses might be more appropriate to capture the uncertainty in the BIA data set in the overall BIA model.