Methods to quantify the importance of parameters for model updating and distributional adaptation

Purpose Decision models are time consuming to develop, therefore adapting previously developed models for new purposes may be advantageous. We provide methods to prioritise efforts to 1) update parameter values in existing models, 2) adapt existing models for distributional cost effectiveness analysis (DCEA). Methods Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and one-way sensitivity analysis (OWSA). We apply 1) VOI to prioritise searches for additional information to update parameter values and 2) OWSA to prioritise searches for parameters which may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics which quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study. Results In our case study, updating 2 out of 21 probabilistic model parameters, addressed 71.5% of the total VOI, and updating three addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the three parameters that should be prioritised. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for over 95% of the total variation, suggesting efforts should be aimed towards these. Conclusions These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters.