Evidence map›Paper›PMID 38721872›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2024

A Comparison of Additional Benefit Assessment Methods for Time-to-Event Endpoints Using Hazard Ratio Point Estimates or Confidence Interval Limits by Means of a Simulation Study.

Christopher A Büsch, Marietta Kirchner, Rouven Behnisch, Meinhard Kieser

Abstract readComparative Study
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Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Christopher A BüschInstitute of Medical Biometry (IMBI), Department Medical Biometry, Heidelberg University, Heidelberg, Germany.ORCID 0000-0003-2664-7147
Marietta KirchnerInstitute of Medical Biometry (IMBI), Department Medical Biometry, Heidelberg University, Heidelberg, Germany.ORCID 0000-0003-0294-2483
Rouven BehnischInstitute of Medical Biometry (IMBI), Department Medical Biometry, Heidelberg University, Heidelberg, Germany.ORCID 0000-0002-1131-9485
Meinhard KieserInstitute of Medical Biometry (IMBI), Department Medical Biometry, Heidelberg University, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor time-to-event endpoints, three additional benefit assessment methods have been developed aiming at an unbiased knowledge about the magnitude of clinical benefit of newly approved treatments. The American Society of Clinical Oncology (ASCO) defines a continuous score using the hazard ratio point estimate (HR-PE). The European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) developed methods with an ordinal outcome using lower and upper limits of the 95% HR confidence interval (HR-CI), respectively. We describe all three frameworks for additional benefit assessment aiming at a fair comparison across different stakeholders. Furthermore, we determine which ASCO score is consistent with which ESMO/IQWiG category.

methodsIn a comprehensive simulation study with different failure time distributions and treatment effects, we compare all methods using Spearman's correlation and descriptive measures. For determination of ASCO values consistent with categories of ESMO/IQWiG, maximizing weighted Cohen's Kappa approach was used.

resultsOur research depicts a high positive relationship between ASCO/IQWiG and a low positive relationship between ASCO/ESMO. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 corresponds to ESMO categories. Using ASCO values of 21 and 38 as cutoffs represents IQWiG categories. LIMITATIONS: We investigated the statistical aspects of the methods and hence implemented slightly reduced versions of all methods.

conclusionsIQWiG and ASCO are more conservative than ESMO, which often awards the maximal category independent of the true effect and is at risk of overcompensating with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and underpowered/overpowered studies influence all methods in some degree. Nevertheless, ESMO is the most liberal one. HIGHLIGHTS: For the additional benefit assessment, the American Society of Clinical Oncology (ASCO) uses the hazard ratio point estimate (HR-PE) for their continuous score. In contrast, the European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) use the lower and upper 95% HR confidence interval (HR-CI) to specific thresholds, respectively. ESMO generously assigns maximal scores, while IQWiG is more conservative.This research provides the first comparison between IQWiG and ASCO and describes all three frameworks for additional benefit assessment aiming for a fair comparison across different stakeholders. Furthermore, thresholds for ASCO consistent with ESMO and IQWiG categories are determined, enabling a comparison of the methods in practice in a fair manner.IQWiG and ASCO are the more conservative methods, while ESMO awards high percentages of maximal categories, especially with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and under/-overpowered studies influence all methods. Nevertheless, ESMO is the most liberal one. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 correspond to the categories of ESMO. Using ASCO values of 21 and 38 as cutoffs represents categories of IQWiG.

Indexed as

Proportional Hazards ModelsComputer SimulationConfidence IntervalsHumansMedical Oncologyadditional benefit assessment methodsASCOESMOIQWiGsimulation studytime-to-event endpoints

Identifiers

PMID38721872
PMCPMC11102642

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