Presentation (CTI)

OBJECTIVES: Developing health economic models can be a time- and resource-consuming process. In an era of increasing automation, the question of whether this process can be accelerated arises. This study aimed to explore the potential of a Microsoft (MS) Excel-based tool for automation of cost-effectiveness models programming.
METHODS: An automated Markov cohort model generator was developed, utilizing a combination of Visual Basic for Applications (VBA) and R programming languages to prepare health economic models. Users provide input parameters into a pre-specified MS Excel workbook and then run an R script to generate a model in MS Excel. The tool is based on a fully deterministic algorithm, meaning it always produces the same results for a fixed input. This generator was employed to replicate two Markov models. The first one assessed the cost-effectiveness of two innovative prostheses for total hip replacement compared to a standard prosthesis. The second model compared the cost-effectiveness of combination therapy (lamivudine and zidovudine) with zidovudine alone in a four-state HIV/AIDS model.
RESULTS: The model generator successfully replicated both models on the first attempt. The replicated models were error-free, and their results matched those of the manually programmed models. The automated programming process for these models took 90 minutes in the first example and 28 in the second example. Both replicated models were capable of performing probabilistic sensitivity analysis and deterministic sensitivity analysis.
CONCLUSIONS: Utilizing the generator was relatively fast, straightforward, and intuitive. This way of programming decreases risk of a human error while being fully deterministic, increasing confidence in the produced results without the need for repeated testing. While AI may surpass this approach in the coming years, for now the generator can serve as a viable compromise between manual model programming and its automation.