Presentation (CTI)

OBJECTIVES: Over the last six years, Microsoft 365 Excel has introduced advanced spreadsheet functions like LAMBDA, SEQUENCE, and XLOOKUP that enable transparent, modular, and powerful model building without macros or add-ins. These functions can mitigate some of the valid criticisms the R for HTA movement has about the use of Excel in health economic models. Yet uptake in HTA practice remains limited, likely because perpetual editions (e.g., Office 2019/2021) lack these functions and how widely stakeholders have moved to Microsoft 365 is unknown. This study demonstrates their application in Markov models with probabilistic sensitivity analysis (PSA).
METHODS: We developed a fully functional cohort state-transition model in a compact suite of named functions using LAMBDA and REDUCE to simulate a disease progression trace. A macro-free PSA was implemented using RAND and distribution functions (BETA.INV, GAMMA.INV), dynamically linked to model parameters through reusable functions. SEQUENCE and XLOOKUP eliminated helper columns and enabled time-varying inputs for mortality and utility. Excel Labs’ Advanced Formula Editor was used for ease of formula development and review. The workbook was cloud-compatible, requiring no local scripting.
RESULTS: Microsoft 365 functions enabled compact and interpretable models, where core logic was embedded in named functions rather than spread across dozens of cell references. Macro-free PSA improved run-time efficiency, and evaluators could audit calculations without excess scrolling or formula tracing. Nonetheless, knowledge gaps and inconsistent payer expectations may limit uptake. Reviewer access, familiarity, and support documentation remain critical barriers.
CONCLUSIONS: Modern Excel functions can significantly improve the transparency, flexibility, and reproducibility of HTA models. Broader adoption will require both training and trust, but these tools have the potential to reshape HTA modeling beyond legacy workflows.