Judgements based on average cost-effectiveness estimates may disguise significant heterogeneity in net health outcomes. Decisions about coverage of new interventions are often more efficient when they consider between-patient heterogeneity, which is usually operationalized as different selections for different subgroups. While most model-based cost-effectiveness studies are populated with aggregated-level sub-group estimates, individual-level data are recognized as the best source of evidence to produce unbiased and efficient estimates to explore this heterogeneity. This paper extends a previously published framework to assesses the added value of having access to individual-level data, compared to using aggregate-level data only, in the absence/presence of mutually exclusive population subgroups. Supported by a case study on the cost-effectiveness of interventions to increase uptake of smoke-alarms, the extended framework provided a quantification of the benefits foregone of not using individual-level data, pointed to the optimal number of subgroups and where further research should be undertaken. Although not indicating changes in reimbursement decisions, results showed that irrespective of using aggregate or individual-level data, no substantial additional gains are obtained if more than two subgroups are taken into account. However, depending on the evidence type used, different subgroups are revealed as warranting larger research funds. The use of individual-level data, rather than aggregate, may however influence not only the extent to which an appropriate understanding of existing heterogeneity is attained, but, more importantly, it may shape approval decisions for particular population subgroups and judgements of future research.