Methodological problems in the method used by IQWiG within early benefit assessment of new pharmaceuticals in Germany

Matthias Herpers and Charalabos-Markos Dintsios ()
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Matthias Herpers: ClinStat GmbH
Charalabos-Markos Dintsios: Heinrich-Heine University Düsseldorf

The European Journal of Health Economics, 2019, vol. 20, issue 1, No 4, 45-57

Abstract: Abstract Background The decision matrix applied by the Institute for Quality and Efficiency in Health Care (IQWiG) for the quantification of added benefit within the early benefit assessment of new pharmaceuticals in Germany with its nine fields is quite complex and could be simplified. Furthermore, the method used by IQWiG is subject to manifold criticism: (1) it is implicitly weighting endpoints differently in its assessments favoring overall survival and, thereby, drug interventions in fatal diseases, (2) it is assuming that two pivotal trials are available when assessing the dossiers submitted by the pharmaceutical manufacturers, leading to far-reaching implications with respect to the quantification of added benefit, and, (3) it is basing the evaluation primarily on dichotomous endpoints and consequently leading to an information loss of usable evidence. Objective To investigate if criticism is justified and to propose methodological adaptations. Methods Analysis of the available dossiers up to the end of 2016 using statistical tests and multinomial logistic regression and simulations. Results It was shown that due to power losses, the method does not ensure that results are statistically valid and outcomes of the early benefit assessment may be compromised, though evidence on favoring overall survival remains unclear. Modifications, however, of the IQWiG method are possible to address the identified problems. Conclusion By converging with the approach of approval authorities for confirmatory endpoints, the decision matrix could be simplified and the analysis method could be improved, to put the results on a more valid statistical basis.

Keywords: IQWiG; Early benefit assessment; Benefit quantification; Dichotomization; Pivotal trials; Statistical validity (search for similar items in EconPapers)
JEL-codes: I18 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10198-018-0981-3

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