ReviewPharmacoEconomics2025
Defining Biological and Clinical Plausibility: The DICSA Framework for Protocolized Assessment in Survival Extrapolations Across Therapeutic Areas.
Review in PharmacoEconomics, 2025. 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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5 authors.
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Abstract
backgroundNumerous health technology assessment guidance documents emphasize the importance of biological/clinical plausibility of modeled lifetime incremental survival without clearly defining it.
objectivesThis paper defines biologically and clinically plausible lifetime survival extrapolations and proposes a framework to systematically assess this by comparing survival expectations estimated premodeling, with the final modeled survival extrapolations. This framework is embedded in a survival extrapolation protocol template, which ensures that both the expectations and extrapolations are based on unified, comprehensive evidence.
methodsA targeted review was conducted of 29 guidance documents from National Institute for Health and Care Excellence, Pharmaceutical Benefits Advisory Committee, Haute Autorité de Santé, Canada's Drug Agency, and European joint clinical assessment, focusing on survival analysis, evidence synthesis, cost-effectiveness modeling methods, and use of observational data.
resultsSurvival extrapolations are biologically/clinically plausible when "predicted survival estimates that fall within the range considered plausible a-priori, obtained using a-priori justified methodology." These a priori expectations should utilize the totality of evidence available and take into account local target setting (i.e., survival-influencing aspects such as patient population, treatment pathway, and country). Pre-protocolized biologically/clinically plausible survival extrapolation was operationalized in a five-step DICSA approach: (1) Describe the target setting as defined by all relevant treatment and disease aspects that influence survival; (2) collect Information from relevant sources; (3) Compare survival-influencing aspects across information sources; (4) Set pre-protocolized survival expectations and plausible ranges; and (5) Assess how trial-based extrapolations align with the set expectations by comparing modeled survival extrapolations to the range of values a priori considered to be plausible.
conclusionThe definition of plausibility of survival extrapolations, the operationalization of its assessment, and the corresponding extrapolation protocol template can contribute to the transparent development of biologically/clinically plausible survival extrapolations.
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