The Use of Baseline Score in Cost-Effectiveness Modeling of Competing Interventions

Network meta-analysis (NMA) with individual participant data can estimate how treatment effects change with patient characteristics. Yet cost-effectiveness analyses typically use population-average effects. We introduce a framework that incorporates NMA-derived heterogeneous treatment effects into cost-effectiveness analysis using a risk-modeling approach.

We first derived a baseline risk score for each patient using a prognostic model. This risk score was then used as an effect modifier in a network meta-regression to estimate risk-specific treatment effects. These effects were incorporated into the cost-effectiveness model to estimate the incremental cost-effectiveness ratios and net monetary benefits as functions of the baseline risk score. We demonstrated the approach using data from observational and randomized studies in relapsing-remitting multiple sclerosis, comparing dimethyl fumarate, glatiramer acetate, and placebo.

Risk-dependent treatment effects from the prediction-NMR framework led to substantial variation in cost-effectiveness across the baseline risk distribution. When these treatment effects were incorporated into the cost-effectiveness model, incremental cost-effectiveness ratios increased steadily across baseline risk quintiles, from 48 811 Swiss Francs [CHF] (US $62 034) /QALY in the lowest-risk group to 212 870 CHF (US $270 536)/QALY in the highest. Dimethyl fumarate has a higher net monetary benefit up to a baseline risk of 55%, after which glatiramer acetate becomes the preferred option.

Our findings show that integrating baseline risk modeling with NMA and cost-effectiveness analysis provides more informative decision-making than relying on average effects. Treatment value can vary substantially across the risk spectrum, indicating that optimal therapy selection is strongly dependent on individual patient risk.