OBJECTIVES: Cost-effectiveness analysis requires comparison of outcomes in treated and untreated populations. Data from randomized clinical trials (RCT) do not provide progression rates representative of the general population, while treatment effects in observational data may be biased due to non-randomization. We developed a novel approach for estimating untreated progression rates (controls) by using data from a nationally representative patient cohort, as well as RCT estimates. METHODS: We used data from the 2000-2005 Sonya Slifka multiple sclerosis (MS) cohort. Disease progression was characterized by disability-based disease states and relapses. We modeled probabilities of disease state transitions using a first-order annual Markov model that adjusted for age, gender, disease duration, recent relapse rates, prior states, and the specific disease-modifying therapy (DMT). We developed an iterative multinomial logistic regression algorithm, constraining the effects of DMT to match those reported by RCTs. RESULTS: After correcting for the DMT treatment effects and other observable risk factors, the probability of disability progression was greater for estimates based on all MS patients compared to the estimates based on untreated individuals only. The 95% confidence intervals using the entire cohort (including treated and untreated individuals) were narrower than the intervals based on the subsample of untreated patients. CONCLUSIONS: Our results indicate that the untreated patients in our study had lower estimates of disease progression than the treated patients would have had if they remained untreated. This suggests that patients who forgo treatment are likely to have milder, slower progressing forms of MS. Correcting for treatment effects in a more inclusive group of patients likely provides a more realistic estimate of disease progression than simply characterizing progression in an untreated cohort. The use of a broader cohort also improves the precision of disease progression estimates.