Baseline Risk Score in cost-effectiveness modelling
This method improves cost-effectiveness analysis by considering how a patient's individual risk level affects treatment outcomes. Instead of using average results across all patients, it uses a baseline risk score to predict how well treatments work for people at different risk levels, leading to better personalized treatment decisions.
At a glance
Use when
Conducting cost-effectiveness analyses where treatment benefits are expected to vary by patient risk; when individual participant data are available; for informing stratified or personalized healthcare policies.
Avoid when
Individual participant data are unavailable; when baseline risk does not modify treatment effect; in settings with limited data on prognostic factors; for very rare conditions where risk stratification is not feasible.
Inputs
Individual participant data (IPD) from clinical trials or observational studies, prognostic factors for outcome prediction, treatment effect estimates from network meta-analysis, health state utilities, cost data, and willingness-to-pay thresholds.
Outputs
Risk-specific treatment effects, risk-stratified incremental cost-effectiveness ratios (ICERs), net monetary benefits (NMBs) as a function of baseline risk, and risk-dependent treatment recommendations.
How it works
The method combines individual participant data network meta-analysis (IPD-NMA) with prognostic risk modelling. A baseline risk score is derived for each patient using a prognostic model and then used as an effect modifier in network meta-regression (NMR) to estimate risk-specific treatment effects. These heterogeneous treatment effects are integrated into a cost-effectiveness model to compute risk-stratified incremental cost-effectiveness ratios (ICERs) and net monetary benefits (NMBs). The approach was applied in relapsing-remitting multiple sclerosis using data from randomized and observational studies comparing dimethyl fumarate, glatiramer acetate, and placebo.
- Project
- HTx
- Funding
- Horizon 2020
- Project status
- Completed 2024
- HTA domains
- Costs & Economic Evaluation
- Categories
- HeterogeneityRWE
- Technology
- Medicines
- Assumptions
- The prognostic model accurately captures baseline risk; treatment effect modification by risk is consistent across interventions; linearity or specified functional form of risk-treatment interaction; availability of individual participant data for network meta-regression.
- Strengths
- Enables personalized cost-effectiveness analysis; captures heterogeneity in treatment effects; improves decision-making by moving beyond population averages; leverages IPD for more precise estimates; allows identification of subgroups where interventions offer the greatest value.
- Limitations
- Requires access to individual participant data, which may be limited; depends on quality of prognostic model; assumes correct specification of risk-effect relationship; may not be generalizable if risk predictors differ across populations.
- Also known as
- Prediction-NMR framework, risk-stratified cost-effectiveness analysis, baseline risk modelling in NMA
Questions this answers
- › How does a patient's baseline risk affect the cost-effectiveness of different treatments?
- › Can we personalize treatment recommendations based on individual risk in economic evaluations?
- › Why might a treatment be cost-effective for high-risk patients but not for low-risk ones?
- › How can network meta-analysis be combined with cost-effectiveness models using patient-level risk?
- › What is the impact of using average treatment effects versus risk-specific effects in economic models?
- › At what risk level does one treatment become more valuable than another?
References & sources
- deliverableec.europa.eu ↗
- paperDOI: 10.1016/j.jval.2026.03.2245 ↗
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