An MCDA approach for identifying key relationships between multiple sclerosis health states

Abstract

Objective: To identify key relationships between health states in Markov chains for two untreated multiple sclerosis patients’ groups: younger than 28 years; and aged 28 or over. After that, this study seeks to observe if the identified key relationships between health states for the two groups are the same. Methods: The Markov chains performed by Palace et al. for multiple sclerosis with 10 health states by annual cycles for the two groups cited above have been used to identify the key relationships between these states through a multi-criteria decision analysis (MCDA) approach. Results: The MCDA approach has identified 34 key relationships for both patients’ groups. By the way, the key relationship from health state #6 to health state #8 and the key relationship from health state #8 to health state #9 have been identified only for the younger than 28 years patients’ group Markov chain. Conclusion: The MCDA approach here presented has showed a way for identifying key relationships between health states in Markov chains. This study has demonstrated that the progression risk in multiple sclerosis from the health state #6 to health state #8 and from health state #8 to health state #9 can be bigger for the untreated patients’ group younger than 28 years than the patients’ group aged 28 or over, through an MCDA approach.