📊 Many models are created for #ClinicalPrediction every year, but not all of them will actually lead to better outcomes for patients. This gap between the models that are created and those that are useful can come at huge costs and a waste of research effort – and it was what a team at @QUT’s @AusHSI set out to address with their #R software package #predictNMB. “predictNMB can be used to evaluate a real or hypothetical prediction model in health economic terms. By adjusting the inputs to the functions in predictNMB, you may find that whether models are useful may depend heavily on the treatment available, the costs associated with treatment, or the prevalence of the event being predicted,” said @RexParsons8, a PhD candidate at @QUTphsw, who’s part of the predictNMB team. predictNMB has received great feedback from both academics and people working in industry. Earlier this year, the team was named by @StatSocAus the runner-up for the ARDC-sponsored Venables Award for New Developers of #OpenSource Software for #DataAnalytics for developing the tool. “I think it was … improved a lot by going through review at @rOpenSci and making adjustments to abide by the community’s high standards,” said Rex of predictNMB’s success. 🔗 Read more about the team and their work in the latest edition of our interviews with leading research software engineers (#RSEs) in Australia 👉 ardc.edu.au/article/shaping-… Also in the predictNMB team are @RobinBlythe5, Prof Adrian Barnett, A/Prof @SusannaCramb and Prof @StevenMMcPhail. #ResearchSoftwareAU #RSE #HealthEconomics #health #medical #research #software