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Quantitative risk assessment of novel Modified Risk Tobacco Products (MRTP) must rest on indirect measurements that are indicative of disease development prior to epidemiological data becoming available. For this purpose, a Population Health Impact Model (PHIM) has been developed to estimate the reduction in the number of deaths from smoking-related diseases following the introduction of an MRTP. One key parameter of the model, the F-factor, describes the effective dose upon switching from cigarette smoking to using an MRTP.Biomarker data, collected in clinical studies, can be analyzed to estimate the effects of switching to an MRTP as compared to quitting smoking. Based on transparent assumptions, a link function is formulated that translates these effects into the F-factor. The concepts of ‘lack of sufficiency’ and ‘necessity’ are introduced, allowing for a parametrization of a family of link functions. These can be uniformly sampled, thus providing different ‘scenarios’ on how biomarker-based evidence can be translated into the F-factor to inform the PHIM.No epidemiological data are available for novel MRTPs.Risk assessment based on biomarker data is proposed.A Bayesian approach with transparent assumptions is deployed.