When does economic model type become a decisive factor in health technology appraisals? Learning from the expanding treatment options for relapsing-remitting multiple sclerosis

Cost-effectiveness of mepolizumab for severe eosinophilic asthma in China

OBJECTIVE

To evaluate the economic value of mepolizumab as an add-on therapy to the standard of care (SoC) for patients with severe eosinophilic asthma in China.

METHODS

A Markov model with three health conditions was constructed to calculate the incremental cost per quality-adjusted life year (QALY) in mepolizumab with SoC and SoC only groups from the perspective of the Chinese healthcare system throughout an entire lifespan. The model was populated with local costs, while efficacy parameters were obtained from the global Phase III MENSA trial and mortality was derived from two surveys. One-way and probabilistic sensitivity analyses were conducted. Additional scenario analysis was used to estimate the cost-effectiveness impact of changes in the price of mepolizumab.

RESULTS

Over the lifetime treatment horizon, the incremental cost-effectiveness ratio (ICER) of mepolizumab plus SoC compared to SoC alone was $170 648.73 per QALY. Sensitivity analyses focused on these results. Scenario analysis showed that mepolizumab would require a price reduction of at least 82% to reach the current willingness-to-pay (WTP=$38 223.34/QALY) threshold.

CONCLUSION

Mepolizumab is not a cost-effective healthcare resource in China at its current pricing.

To wait, or too late? Modeling the effects of delayed ofatumumab treatment in relapsing-remitting multiple sclerosis

BACKGROUND

Several disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) reduce relapse rates and slow disease progression. RRMS DMTs have varying efficacy and administration routes; DMTs prescribed first may not be the most effective on relapses or disease progression. Here, we aimed to quantify the benefit of initiating ofatumumab, a high-efficacy DMT, earlier in the treatment pathway.

METHODS

Aggregate data from a real-world cohort of patients with RRMS, who were eligible for dimethyl fumarate (DMF) or ofatumumab treatment within the UK National Health Service (N = 615), were used to produce a simulated patient cohort. The cohort was tracked through a discrete event simulation (DES) model, based on the Expanded Disability Status Scale (EDSS), with a lifetime time horizon. Outcomes assessed were: mean number of relapses, time to wheelchair (EDSS ≥7), and time to death. Two modeling approaches were used. The first compared outcomes between two treatment sequences (base case: ofatumumab to natalizumab versus DMF to ofatumumab). The second incorporated a time-specific delay of 1-5 years for switching from DMF to ofatumumab; the difference in outcomes as a function of increasing delay to ofatumumab are reported.

RESULTS

Compared with delayed ofatumumab, fewer relapses and increased time to wheelchair were predicted for earlier ofatumumab in the treatment-sequence approach (mean relapses over the lifetime time horizon: 8.63 versus 9.00; time to wheelchair: 17.55 versus 16.60 years). Time to death was similar for both sequences. At Year 10, a numerically greater proportion of patients receiving earlier ofatumumab had mild disease (EDSS 0-3: 44.12% versus 40.06%). Greater differences, reflecting poorer outcomes, were predicted for relapses and time to wheelchair with increasing delays to ofatumumab treatment.

CONCLUSIONS

The DES model provided a means by which the magnitude of benefit associated with earlier ofatumumab initiation could be quantified; fewer relapses and a prolonged time to wheelchair were predicted.

Time-to-event analysis in economic evaluations: a comparison of modelling methods to assess the cost-effectiveness of transplanting a marginal quality kidney

BACKGROUND

Economic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney.

METHODS

Cost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon.

RESULTS

DES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased.

CONCLUSION

Different modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.

How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease

Background. Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. Methods. We describe 4 challenges associated with modeling heterogeneity and structural, stochastic, and parameter uncertainty that can be encountered during the development of DES models. We explain why these are important and how to correctly implement them. To illustrate the impact of the modeling choices discussed, we use (results of) a model for chronic obstructive pulmonary disease (COPD) as a case study. Results. The results from the case study showed that, under a correct implementation of the uncertainty in the model, a hypothetical intervention can be deemed as cost-effective. The consequences of incorrect modeling uncertainty included an increase in the incremental cost-effectiveness ratio ranging from 50% to almost a factor of 14, an extended life expectancy of approximately 1.4 years, and an enormously increased uncertainty around the model outcomes. Thus, modeling uncertainty incorrectly can have substantial implications for decision making. Conclusions. This article provides guidance on the implementation of uncertainty in DES models and improves the transparency of reporting uncertainty methods. The COPD case study illustrates the issues described in the article and helps understanding them better. The model R code shows how the uncertainty was implemented. For readers not familiar with R, the model's pseudo-code can be used to understand how the model works. By doing this, we can help other developers, who are likely to face similar challenges to those described here.