For the benefit assessment of drugs, data is needed for comparison with the therapeutic standard. Since orphan drugs are often approved on the basis of non-comparative data, the legislator introduced the procedure of application-accompanying data collection (AbD) from 2020. The aim is to close existing gaps in evidence and thus obtain a better database for benefit assessment. In the meantime, initial experience from the examination of study documents of pharmaceutical manufacturers is available. They concern content-related and methodological aspects of study planning, study implementation and data evaluation as well as initial interim results from ongoing AbDs that use registries as a data source.

Against this background, the Federal Joint Committee (G-BA) has commissioned IQWiG to scientifically develop selected aspects for the generation and evaluation of care-related data for the early benefit assessment of medicinal products. The focus is on seven core topics for conducting non-randomized comparative studies, including identifying confounders (variables that can skew study results), collecting patient-reported endpoints (PROs), and propensity score analyses in areas of application with small patient groups.

IQWiG’s conclusion: In compliance with internationally recognised quality requirements, studies based on registry data can be helpful for benefit assessment, e.g. in the case of orphan drugs. It is worthwhile to invest more effort in the early and good planning of such studies and the data infrastructure. This saves a lot of work when conducting the study and increases efficiency in the evaluation of the data collected.

The Rapid Report shows ways in which non-randomized comparative studies can be carried out in a feasible way, but also reveals that they are much more complex than randomized controlled trials. It is important to minimise a possible bias as a result of the non-randomised comparison: For example, for studies without randomisation – i.e. without the random allocation of patients to the study arms – much larger numbers of participants are required due to the necessary adjustment for confounders and thus an increased survey effort.

“If you invest intensively in the study planning of a non-randomised study at the beginning, you can not only save effort in carrying it out, but also reap more meaningful data,” says Volker Vervölgyi, Head of Division in IQWiG’s Medicines Assessment Division, summarising the recommended procedure for data collection for new medicines during the application, adding: “However, an important basis for this is a good research data infrastructure, which is often still lacking in this country.”

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[A25-13 ] Scientific elaboration of selected aspects for the generation of care-related data and their evaluation for the purpose of the benefit assessment of medicinal products according to § 35a SGB V

Editor: X-Press Journalistenbüro GbR

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