An Innovative Approach to Modelling the Optimal Treatment Sequence for Patients with Relapsing–Remitting Multiple Sclerosis: Implementation, Validation, and Impact of the Decision-Making Approach

Abstract

Introduction An innovative computational model was developed to address challenges regarding the evaluation of treatment sequences in patients with relapsing–remitting multiple sclerosis (RRMS) through the concept of a ‘virtual’ physician who observes and assesses patients over time. We describe the implementation and validation of the model, then apply this framework as a case study to determine the impact of different decision-making approaches on the optimal sequence of disease-modifying therapies (DMTs) and associated outcomes. Methods A patient-level discrete event simulation (DES) was used to model heterogeneity in disease trajectories and outcomes. The evaluation of DMT options was implemented through a Markov model representing the patient’s disease; outcomes included lifetime costs and quality of life. The DES and Markov models underwent internal and external validation. Analyses of the optimal treatment sequence for each patient were based on several decision-making criteria. These treatment sequences were compared to current treatment guidelines. Results Internal validation indicated that model outcomes for natural history were consistent with the input parameters used to inform the model. Costs and quality of life outcomes were successfully validated against published reference models. Whereas each decision-making criterion generated a different optimal treatment sequence, cladribine tablets were the only DMT common to all treatment sequences. By choosing treatments on the basis of minimising disease progression or number of relapses, it was possible to improve on current treatment guidelines; however, these treatment sequences were more costly. Maximising cost-effectiveness resulted in the lowest costs but was also associated with the worst outcomes. Conclusions The model was robust in generating outcomes consistent with published models and studies. It was also able to identify optimal treatment sequences based on different decision criteria. This innovative modelling framework has the potential to simulate individual patient trajectories in the current treatment landscape and may be useful for treatment switching and treatment positioning decisions in RRMS. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-021-01975-5.

Evaluating the Validation Process: Embracing Complexity and Transparency in Health Economic Modelling

“…Terminology in health economic model validation can often be confusing because of different interpretations and a lack of clear definitions. The term “internal validation” can be used to describe the act of comparing model outcomes to empirical data that were used to build the model [ 11 – 14 ]. The same definition is referred to as “dependent validation” elsewhere [ 15 , 16 ].…”

“…However, the same definition is referred to as “independent validation” as well [ 15 , 16 ], whereas “independent validation” has also been employed to indicate validation undertaken by a third party [ 20 ]. “External validation” has been used for the comparison between model outcomes against outcomes produced by other models [ 14 ], but this is often referred to as “cross-validation” [ 1 , 15 , 21 ]. Finally, some publications include a sensitivity analysis as part of model validation [ 22 , 23 ], although a sensitivity analysis aims to explore uncertainty, not validate models: a model full of errors may still produce robust results.…”