Alternative Decision Analysis Modeling in the Economic Evaluation of Tumor Necrosis Factor Inhibitors for Rheumatoid Arthritis

Development and Validation of a Simulation Model for Treatment to Maintain Remission in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

OBJECTIVE

Fixed and tailored rituximab retreatment strategies to maintain remission in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) are associated with tradeoffs. The current study was undertaken to develop a simulation model (AAV-Sim) to project clinical outcomes with these strategies.

METHODS

We developed the AAV-Sim, a microsimulation model of clinical events among individuals with AAV initiating treatment to maintain remission. Individuals transition between health states of remission or relapse and are at risk for severe infection, end-stage renal disease, or death. We estimated transition rates from published literature, stratified by individual-level characteristics. We performed validation using the mean average percent error (MAPE) and the coefficient of variation of root mean square error (CV-RMSE). In internal validation, we compared model-projected outcomes over 28 months with outcomes observed in the Rituximab versus Azathioprine in ANCA-Associated Vasculitis 2 (MAINRITSAN2) trial, which compared fixed versus tailored retreatment. In external validation, we compared outcomes with fixed rituximab retreatment from the AAV-Sim to outcomes from the MAINRITSAN1 trial and an observational study.

RESULTS

The AAV-Sim projected outcomes similar to those in the MAINRITSAN2 trial, including minor (AAV-Sim 6.0% fixed versus 7.3% tailored; MAINRITSAN2 6.2% versus 8.6%; MAPE 3% and 15%) and major relapse (AAV-Sim 3.5% versus 5.5%; MAINRITSAN2 3.7% versus 7.4%; MAPE 5% and 26%), severe infection (AAV-Sim 19.4% versus 11.1%; MAINRITSAN2 19.8% versus 10.2%; MAPE 2% and 9%), and relapse-free survival (AAV-Sim 84.8% versus 82.3%; MAINRITSAN2 86% versus 84%; CV-RMSE 2.3% and 2.5%). Similar performance was observed in external validation.

CONCLUSION

The AAV-Sim projected a range of clinical outcomes for different treatment approaches that were validated against published data. The AAV-Sim has the potential to inform management guidelines and research priorities.

Markov modeling and discrete event simulation in health care: a systematic comparison

OBJECTIVES

The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions.

METHODS

A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted.

RESULTS

Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors' experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time.

CONCLUSIONS

Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.