In the setting of multiple remission and relapse periods of a chronic disease, simple endpoint analysis does not fully capture all relevant information, and we need methods to additionally describe both the duration of remission as well as the interruptions in this desired state. Probably the two-state continuous Markov process model comprises the best mathematical approach to data analysis. However, this approach is complex and not intuitive to clinicians. In this paper we propose a simple scoring system and a graph that can enhance the information about the remission experience in a trial or cohort study.
The continuity rewarded ('ConRew') score sums up periods in remission, and rewards extended periods by placing more value on uninterrupted periods than on interrupted periods. The 'patient vector graph' attempts to plot each patient's remission experience over time as a horizontal line (the 'vector') that is visible when the patient is in remission, but interrupted whenever relapse occurs. In this way a pattern is formed that conveys the number of patients experiencing remission, their individual total duration and interruptions, and time when these occur.
In a dataset of a randomized trial in early rheumatoid arthritis, the graph clearly showed both early and late benefit of one group over the other. The scoring system demonstrated the main benefit was in the number of remission periods, not in their 'uninterruptedness'.
Both approaches proved feasible and added extra information.