In response to the realization that drug science is at best a “progressive reduction of uncertainty,” (Woodcock 2004) recent years have seen a transformation of the clinical trial landscape toward nimbler designs. Master protocols, adaptive designs, the establishment of accelerated approval pathways, and post-market learning systems now allow scientists to accelerate discoveries and to continuously update efficacy estimates. Cost and value assessments, however, are often still conducted based on sparse results from the initial registration trials. Such assessments usually have poor predictive power regarding the ultimate value of a new treatment, because they fail to account for subsequent evidence of efficacy in larger populations and new treatment contexts. To support cost-effective and value-based health care, dynamic cost-effectiveness models that consider the economic value of treatments over the product life cycle are needed. This dynamic approach can be achieved by using Bayesian statistical methods. Such approaches, however, require the statistician to estimate prior probabilities (here, the likelihood of a treatment’s value to increase or decrease over time). The lack of systematic data to inform these estimates has hampered widespread adoption of dynamic assessment methods. The PACE Continuous Innovation Indicators (CII - https://pacenetworkusa.com/continuousinnovation.php) contain extensive historical to present day information about the long-term and evolving value of new cancer medicines. The tool catalogues published evidence of treatments that improve overall survival for 12 tumor types. CII data allow us to generate evidence-based estimates of prior probabilities for transition between different treatment stages (e.g., advanced, invasive, adjuvant) and evolution of treatments (e.g., refinements, use in combinations, new indications). In a linkage with separate data on cost and incidence, these prior probabilities can then serve as inputs to existing cost-models. We envision data from the CII supporting a broad range of value assessment models leading to improved evidence-based and dynamic estimates of value.