PMI is working on developing products with the potential to reduce individual risk and population harm in comparison to smoking combustible cigarettes (RRPs). To quantify the effects that marketing such products may have on the health of the population as a whole, we have developed a Population Health Impact Model (PHIM). One key parameter of the model is an estimate of the RRP-related reduction of the effective dose of exposure, relative to continued smoking of conventional cigarettes and to cessation (F-factor). By parametrization, its value is 1 if the RRP has an excess risk similar to that of smoking and 0 if the RRP has an excess risk equalling that of smoking cessation. Currently, there is no epidemiological data allowing the direct estimation of the F-factor. Nevertheless, pre-clinical and clinical studies are available and key biomarkers measured during these studies are used to estimate the difference between levels in subjects switching to RRP and those quitting smoking, relative to the difference between levels in subjects continuing smoking CCs and those quitting. By means of Bayesian statistical methods, the uncertainty related to these relative biomarker level changes (RCs) are estimated as well. Link functions are introduced to translate the various RCs and their uncertainty into the F-factor. Given the lack of knowledge to determine reasonable link functions, a family of link functions was introduced, defined on a parameter space that can be uniformly sampled. Aggregation of the information coming from the different biomarkers is performed by assigning ‘uniform’ weights to them. The method allows obtaining a distribution of potential F-factors, representing various sources of uncertainty, which can be used as input to the PHIM.