PNS297 CLINICAL AND KEY OPINION LEADERS VALIDATION IN HEALTH ECONOMICS MODEL AND COMPARATIVE EFFECTIVENESS: A GUIDE FOR NON-EXPERT AUDIENCE

Abstract

Clinical validation — or face validation — is considered necessary in health economic (HE) modeling and comparative effectiveness analysis. Evaluation of model assumptions and interpretations by clinical experts provides an estimation of the clinical relevance of HE analysis. No specific framework including timing for this critical, and potentially costly and time-consuming consultation is available. Here we elaborate a framework, including timing for HE model clinical validation. A targeted literature review was conducted to highlight main themes and issues involved in establishing a conceptual framework for time- and cost-effective clinical validation. Results from the literature review emphasized the importance of clinical validation by clinical experts during three main phases of building a HE model: model structure, inputs and assumptions, and results. It was highlighted that face validity should be started early and iteratively within the analysis timeframe; however, no specific schedule or timing has yet been published. We suggest clinical validation in the process of HE model design should take place in three different phases. First, at the initial model design stage to provide clinical expertise about the underlying disease, patients, and treatment pathway. A second clinical consultation following initial efforts at providing model inputs and assumption should also be considered. A final clinical validation should be scheduled when results from the model are available. We recommend three or five clinical experts to be consulted by a modified Delphi panel with three rounds of emails — corresponding to the three phases described — including specific questions, but also utilizing targeted interviews when time and resources permit. Payers and economists commonly accept that clinical validation should be integral to the process of HE model-building. Timing of clinical validation is key to deriving full value to assist in conceptual framework design and filling data gaps.