Dynamic Medicine Prices for Health-Economic Analyses in the Netherlands: A Case for Integrating Pre- and Post-Exclusivity Price Dynamics

Introduction: Medicine prices change over the product life cycle, especially around loss of exclusivity (LoE), yet economic evaluations often assume constant prices, which may bias cost-effectiveness and budget impact estimates. Methods: We developed and internally validated a forecasting approach for Dutch medicine price trajectories for use in cost-effectiveness analyses. Using monthly Farminform sales data (from 2000 onwards) for 384 Anatomical Therapeutic Chemical (ATC) codes meeting the inclusion criteria (>5 years of data, <6 months missing, data after 2017), we derived inflation-adjusted mean prices per defined daily dose. A stacked machine learning ensemble, combining XGBoost, Random Forest, Bayesian additive regression trees, a regularised generalised linear model, and LightGBM gradient boosting, was tuned by ATC-grouped cross-validation to minimise root mean squared error (RMSE), with weights estimated by non-negative penalised regression. Results: Predictors included therapeutic class and subgroup, market entry period, initial price, revenue group, time on market, LoE status and time since LoE, inflation, and competitor availability. On 103 held-out time series, the six-model ensemble achieved an RMSE of 0.199. Predicted trajectories were highly accurate for 26% of products (within ±5% of observed prices) and of medium accuracy for 47% (overall trend captured; most observations within ±12.5%). Poor accuracy (27%) mainly reflected abrupt price shifts linked to policy changes, reimbursement decisions, or pricing negotiations. Conclusions: Forecasting life-cycle medicine price trajectories is feasible for most products and can support more realistic economic evaluations and reimbursement deliberations in the Netherlands. Remaining challenges include anticipating policy-driven discontinuities and improving interpretability of machine-learning-based forecasts.