Interactive Microsimulation for Transparent Screening Policy Decisions: The ProstateSim tool

Population-based screening for chronic diseases requires balancing multiple, often competing outcomes. In prostate cancer screening, key trade-offs include reductions in cancer-specific mortality versus harms such as overdiagnosis and overtreatment of indolent disease, as well as impacts on quality of life, healthcare resource use, and differences between opportunistic and organized screening strategies. These outcomes differ in scale and timing, making transparent comparison difficult and requiring explicit consideration of underlying value judgments. Data-driven microsimulation models provide a structured way to address this complexity. The ProstateSim tool is based on a microsimulation framework that generates individual life histories for a cohort of men, including prostate cancer onset, disease progression, and competing causes of death. The cohort can be further specified by adapting incidence or progression rates. Different predefined screening strategies can be applied, enabling systematic comparison of resulting population-level outcomes under consistent assumptions. Screening strategies can further be adapted via screening age, test frequency, and compliance. The tool additionally allows analysis of clinical outcomes (e.g. distribution of cancer stages at diagnosis), health economic outcomes (NNS, QALYs, costs per capita, ICER), and intuitive visualizations such as mortality curves with and without screening. This presentation introduces the use of interactive simulation tools for evaluating screening policies using ProstateSim as an example. Beyond effectiveness and harm metrics, we explore visualizations that improve interpretability and support a more intuitive understanding of trade-offs. The aim is to enable more comprehensive and transparent discussions on screening policy design that account for the complexity of population-level decision-making.

Speakers/Panellists

Shehzad Ali

Department of Epidemiology and Biostatistics, Western University, London, Canada