Modelling the cost effectiveness of disease-modifying treatments for multiple sclerosis: issues to consider

Abstract

Several cost-effectiveness models of disease-modifying treatments (DMTs) for multiple sclerosis (MS) have been developed for different populations and different countries. Vast differences in the approaches and discrepancies in the results give rise to heated discussions and limit the use of these models. Our main objective is to discuss the methodological challenges in modelling the cost effectiveness of treatments for MS. We conducted a review of published models to describe the approaches taken to date, to identify the key parameters that influence the cost effectiveness of DMTs, and to point out major areas of weakness and uncertainty. Thirty-six published models and analyses were identified. The greatest source of uncertainty is the absence of head-to-head randomized clinical trials. Modellers have used various techniques to compensate, including utilizing extension trials. The use of large observational cohorts in recent studies aids in identifying population-based, 'real-world' treatment effects. Major drivers of results include the time horizon modelled and DMT acquisition costs. Model endpoints must target either policy makers (using cost-utility analysis) or clinicians (conducting cost-effectiveness analyses). Lastly, the cost effectiveness of DMTs outside North America and Europe is currently unknown, with the lack of country-specific data as the major limiting factor. We suggest that limited data should not preclude analyses, as models may be built and updated in the future as data become available. Disclosure of modelling methods and assumptions could improve the transferability and applicability of models designed to reflect different healthcare systems.

Fig. 1. Number of published studies on Cost-Effectiveness Modeling Studies of MS DMTs by year of publications and by sponsor

The number of published studies on the cost-effectiveness modelling of multiple sclerosis disease-modifying treatments by year of publication and by sponsor, 1996–2012. Gov’t government

Fig. 2. Algorithm for estimating the disease state transition probabilities

To estimate the multiplicative treatment effect coefficients [P(tr)] that would produce the same RR ratios of progression probabilities as reported by pivotal RCTs (RR_CT), we kept the progression probabilities without DMT constant (MN Logit 1) while modifying the treatment factors (LT, individual dummy variable for each specific DMT). We implemented an iterative approach by using a numerical grid search algorithm to find a new set of treatment factors that match the published RRs. Next, we re-estimated no DMT transition probabilities using an MN logit model (MN Logit 2) with new treatment effects, calculated post-estimation RR ratios and modified the treatment effects if necessary. By iteratively adjusting transition probabilities (in MN Logit 2) without DMT and treatment effects, we eventually approached the values that best match the RRs of disease progression from the literature (e < 0.001). DMT disease-modifying treatment, LT treatment effect, MN multinomial, P(O) probability of progressing from current disease state, RCT randomized clinical trial, RR relative risk

Fig. 3. ICERs presented by the length of the time horizon modelled

Reproduced from Noyes et al., with permission. ICER incremental cost-effectiveness ratio