title: Methodological approach to determine minor, considerable, and major treatment effects in the early benefit assessment of new drugs.
abstract: At the beginning of 2011, the early benefit assessment of new drugs was introduced in Germany with the Act on the Reform of the Market for Medicinal Products (AMNOG). The Federal Joint Committee (G-BA) generally commissions the Institute for Quality and Efficiency in Health Care (IQWiG) with this type of assessment, which examines whether a new drug shows an added benefit (a positive patient-relevant treatment effect) over the current standard therapy. IQWiG is required to assess the extent of added benefit on the basis of a dossier submitted by the pharmaceutical company responsible. In this context, IQWiG was faced with the task of developing a transparent and plausible approach for operationalizing how to determine the extent of added benefit. In the case of an added benefit, the law specifies three main extent categories (minor, considerable, major). To restrict value judgements to a minimum in the first stage of the assessment process, an explicit and abstract operationalization was needed. The present paper is limited to the situation of binary data (analysis of 2 × 2 tables), using the relative risk as an effect measure. For the treatment effect to be classified as a minor, considerable, or major added benefit, the methodological approach stipulates that the (two-sided) 95% confidence interval of the effect must exceed a specified distance to the zero effect. In summary, we assume that our approach provides a robust, transparent, and thus predictable foundation to determine minor, considerable, and major treatment effects on binary outcomes in the early benefit assessment of new drugs in Germany. After a decision on the added benefit of a new drug by G-BA, the classification of added benefit is used to inform pricing negotiations between the umbrella organization of statutory health insurance and the pharmaceutical companies.
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Guido
authors.initials: G
authors.lastname: Skipka
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Beate
authors.initials: B
authors.lastname: Wieseler
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kaiser
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Stefanie
authors.initials: S
authors.lastname: Thomas
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Ralf
authors.initials: R
authors.lastname: Bender
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Windeler
authors.affiliation: Institute for Quality and Efficiency in Health Care (IQWiG), 50670, Cologne, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Lange
mesh_terms: D001699:Biometry / Q000379:methods*; D017277:Drug Approval; D004345:Drug Industry / Q000331:legislation & jurisprudence; D004358:Drug Therapy; D033161:Government Regulation; D006801:Humans; D009010:Monte Carlo Method; D018570:Risk Assessment
keywords: Added benefit; Clinical relevance; Early benefit assessment; Magnitude of effects; Shifted hypotheses