Establishing a Victorian linked dataset for budget impact modelling of new cancer treatments
Using real-world data, the group have established a framework to quantify the population health economic impact of new cancer treatments for colorectal cancer, non-small cell lung cancer and melanoma.
DR FANNY FRANCHINI
Research Fellow
Cancer Health Services Research, UMCCR & MSPGH
The listing of new cancer treatments in the Pharmaceutical Benefits Scheme (PBS) and Medical Benefits Schedule (MBS) has become a very complicated undertaking, because of the many uncertainties in the submitted evidence to support listing for increasingly smaller populations. Internationally, several initiatives are taken to use real-world population level data to better understand the uptake of new treatments and to facilitate the approval process.
The MRFF funded "PRedicting the population health economic IMpact of new CAncer Treatments” (PRIMCAT) program aims to estimate population health economic impact of new cancer treatments ahead of market approval. We take a data-driven modelling approach to quantify the population health economic impact of new cancer treatments for several cancer types.
Clinical registries, real-world hospital, and administrative data for state-wide retrospective patient cohorts (n=200,000 over 10 years) are analysed for patient characteristics, treatment patterns and associated costs. Potential future cancer treatments are screened using horizon scanning and multi-criteria decision analysis, with inputs from expert and consumer panels.
Dr Fanny Franchini obtained her PhD in Clinical Medicine from the University of Oxford, during which she focused on the tumour microenvironment in colorectal cancer. Following this experience, Fanny decided to work at the interface between statistics and cancer health science combining her passion for cancer research and data-driven work. She joined Health Data Insight and worked in collaboration with Public Health England on multiple data science projects, focusing on analysing large sets of prescription history data with the aim of inferring symptoms and co-morbidities in cancer patients. Fanny joined the Cancer Health Services Research Unit in 2019 where she works on mapping treatment sequences in several cancers and analysing patient outcomes, as well as building data-driven predictive models to inform treatment decisions.