Modeling therapeutic strategies in rheumatoid arthritis: use of decision analysis and Markov models

The management of patients with rheumatoid arthritis (RA) is controversial, with a number of different proposed treatment strategies based on different conceptions of the natural history of the disease and different interpretations of the efficacy and effectiveness of the drugs used for treatment. We attempted to develop a theoretical framework to assess the effectiveness of different treatment regimens for RA.

We used decision analysis to structure the problem of comparing sequential monotherapy to a combination strategy. Subsequently, we used 3 different estimates of drug effectiveness: one from expert rheumatologists; a metaanalysis; and a recent nationwide survey of American rheumatologists, in a Markov model. Last, we utilized published duration of therapy data to model drug treatment over time.

Estimates of drug effectiveness differed substantially among rheumatologists, but regardless of the estimates and the treatment strategy used, the model predicted over 90% of patients improved by the 3rd drug trial. Over time, treatment patterns in our model resemble the "sawtooth" pattern previously observed.

Treatment strategies in RA are difficult to model because of uncertainty in both the structure of the model and the data needed to perform the analysis. These models tend to overestimate the effectiveness of drug sequences because of nonindependence between therapies, probably due to sequence effects, a change in responsiveness over time, or resistant subgroups. Our preliminary analysis suggests that the most effective agent, possibly methotrexate, should be used first if the objective is to get as many patients into remission as quickly as possible.