Economic model of Alzheimer's disease that incorporates the uncertainty associated with measuring efficacy in clinical trials
Javier Mar, Arantzazu Arrospide, Ron Handels, Myriam Soto-GordoaBackground
Clinical trials of Alzheimer's disease (AD) treatments are ongoing, and uncertainty about their efficacy is a key factor in their evaluation.
Objective
The objective of this study was to propose a new methodological approach for the economic evaluation of treatments to account for the uncertainty associated with measuring efficacy in clinical trials and the waning effect.
Methods
A discrete event simulation model was built using data from a synthetic clinical trial dataset to model typical patient-level natural history trajectories of Clinical Dementia Rating scores from mild cognitive impairment to severe dementia using mixed regression models for repeated measures (MMRM). As the MMRM coefficients are correlated, this variability was incorporated into the model probabilistic sensitivity analysis using Cholesky decomposition. Uncertainty about treatment effect waning was addressed by scenario analysis (optimistic and pessimistic).
Results
Although the main contribution of this study is to describe an innovative model, the results of the hypothetical intervention are presented in the standard format of incremental cost-utility ratios (ICURs), cost-effectiveness and acceptability curves. Specifically, the ICUR of the hypothetical treatment ranged from €73,216 to €63,662 per quality-adjusted life year.
Conclusions
We present an innovative approach to the economic evaluation of Alzheimer's disease treatments, applying a Monte Carlo simulation approach (PSA) to two scenarios and shaping individual cognitive trajectories on a continuous scale to fit the target population of clinical trials. Its transparent design allows the economic model to be shared between the pharmaceutical company and the assessment agencies while keeping individuals’ clinical trial data confidential.