Applying GRADE Criteria to Clinical Inputs to Cost-Effectiveness Modeling Studies

Alexander Mensch, Tanja Beck, Daniele Civello, Christopher Kunigkeit, Nicole Lachmann, Stephanie Stock, Afschin Gandjour and Dirk Müller ()
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Alexander Mensch: Janssen-Cilag GmbH, Johnson & Johnson Platz 1
Tanja Beck: The University Hospital of Cologne (AöR)
Daniele Civello: The University Hospital of Cologne (AöR)
Christopher Kunigkeit: The University Hospital of Cologne (AöR)
Nicole Lachmann: The University Hospital of Cologne (AöR)
Stephanie Stock: The University Hospital of Cologne (AöR)
Afschin Gandjour: Frankfurt School of Finance and Management
Dirk Müller: The University Hospital of Cologne (AöR)

PharmacoEconomics, 2018, vol. 36, issue 8, No 9, 987-994

Abstract: Abstract Background Concerns have been raised about the use of clinical data in cost-effectiveness models. The aim of this analysis was to evaluate the appropriate use of data on clinical effectiveness in cost-effectiveness modeling studies that were published between 2001 and 2015. Methods Assessors rated 72 modeling studies obtained from three therapeutic areas by applying criteria defined by the Grading of Recommendations Assessment, Development and Evaluation group for assessing the quality of clinical evidence: selection of clinical data (publication bias), imprecision, indirectness, inconsistency (i.e., heterogeneity), and study limitations (risk of bias). For all parameters included in the analyses, potential changes over time were assessed. Results Although three out of four modeling studies relied on randomized controlled trials, more than 60% of the modeling studies were based on clinical data with a high or unclear risk of bias, in more than 80%, a risk of publication bias was found, and in about 30%, evidence was based on indirect clinical evidence, having significantly increased over the years. Study limitations were inadequately described in more than one third of the studies. However, less than 10% of clinical studies showed inconsistency or imprecision in study results. Conclusion Despite the fact that the majority of economic evaluations are based on precise and consistent randomized controlled trials, their results are often affected by limitations arising from methodological shortcomings in the underlying data on clinical efficacy. Modelers and assessors should be more aware of aspects surrounding the quality of clinical evidence as considered by the Grading of Recommendations Assessment, Development and Evaluation group.

Date: 2018
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DOI: 10.1007/s40273-018-0651-4

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