A model-based meta-analysis (MBMA) is a type of meta-regression that uses nonlinear mixed-effects models estimated on trial-level data to relate patient and trial characteristics, dosing, biomarkers, and outcomes of treatment.
To use a pharmacometric MBMA within a pharmacoeconomic model of chronic obstructive pulmonary disease (COPD).
A Markov microsimulation model was developed to estimate monthly changes in the key disease severity metrics of COPD (forced expiratory volume in 1 second [FEV ] and exacerbations) to compare a hypothetical drug that increases FEV to usual care. The MBMA was used to predict a baseline exacerbation rate in a group of actual trial patients, given their known baseline FEV . The hypothetical drug increased FEV , thereby decreasing individuals’ predicted exacerbation rates. Individual patient simulations allowed stochastic changes in monthly FEV decline.
In a sample of 1097 trial patients with a mean FEV of 50%, the MBMA predicted 0.93 exacerbations per year on average. The exacerbation rate ranged from 0.52 to 1.3 per year across moderate and severe patient subgroups. A hypothetical anti-inflammatory drug that increased FEV by 50 ml decreased exacerbations by 26%. Given a simplified estimation of costs and quality-adjusted life-years (QALYs) associated with COPD, a drug with a 50-ml increase priced at €35/mo had an incremental cost-effectiveness ratio ranging from €13,000/QALY to approximately €207,000/QALY across patient severity subgroups.
The synergistic aspects of MBMA and pharmacoeconomic modeling are highlighted in this hypothetical example. Markov microsimulation modeling allows the finer predictions of MBMA to inform parameters. Such an approach has utility in both early-phase cost-effectiveness estimations and trial design.