Systematic assessment of decision models in Parkinson's disease

Objective: To give an insight into the structural and methodological approaches used in published decision-analytic models evaluating interventions in Parkinson's disease (PD) and to derive recommendations for future comprehensive PD decision models. Methods: A systematic literature review was performed to identify studies that evaluated PD interventions using mathematical decision models. Using a standardized assessment form, information on the study design, methodological framework, and data sources was extracted from each publication and systematically reported. Strengths and limitations were assessed. Results: We identified eight studies that used mathematical models to evaluate different pharmaceutical (n = 7) and surgical (n = 1) treatment options in PD. All models included economic evaluations. Modeling approaches comprised mathematical equations, decision trees, and Markov models with a time horizon ranging from 5 years to lifetime. All based progression on the evolution of clinical surrogate endpoints. Treatment effects were either modeled via reduction of symptomatic progression and/or initial symptomatic improvement or via reduction of adverse effect rates. No model is currently available that encompasses both the underlying biologic disease progression and the spectrum of all relevant complications and also links them to patient preferences and economic outcomes. Conclusions: Models have been successfully applied to evaluate PD treatments. However, currently available models have substantial limitations. We recommend that a comprehensive, generic, and flexible decision model for PD that can be applied to different treatment strategies should consider a large spectrum of clinically relevant outcomes and complications of the disease during a sufficiently long time horizon, include PD-specific mortality, systematically evaluate uncertainty including heterogeneity effects, and should be validated by independent data or other models. Approaches to model treatment effects included reduction of symptomatic progression, initial symptomatic improvement, or reduction of adverse effects. We believe that structural bias could be avoided if underlying disease progression and treatment effects on symptoms are modeled separately.