OBJECTIVES: Evaluation of cost-effectiveness for chronic disease treatment requires development and validation of a model of disease progression using “real world” data.  We constructed a Markov model using patient-level data from three large studies of glaucoma treatment and conducted internal validation. METHODS: Glaucoma severity and disease progression were defined clinically in terms of visual field loss expressed as mean deviation (MD) measured in decibels (dB). Patient level data for the model came from the Collaborative Initial Glaucoma Treatment Study (CIGTS n=574), the Ocular Hypertension Treatment Study (OHTS n=1,546), and the Advanced Glaucoma Intervention Study (AGIS n=580). Our initial model was limited to the pattern of progression over seven years. Transition probabilities for the Markov model were calculated for each combination of year and MD. The model was estimated with TreeAge software using a microsimulation approach. Internal validation was conducted by comparing the predicted value of hypothetical participants to that of the actual study participants. For this purpose, a clinically significant difference was considered to be 3 decibels (dB) of MD. RESULTS: Three variables---age, race, and starting MD---were most strongly associated with change in MD. Predicted values from the model were regressed on actual study results.  The R2  for the right eye was 0.72, and for the left 0.70. Of those participants outside of a 3 dB band around “perfect” prediction, over 85% had less severe disease at year 7 than predicted by the model.   CONCLUSIONS: Our initial results indicate that the glaucoma progression model properly predicts the result of disease progression in over 80% of “participants”. This suggests that our modeling approach provides a reasonable reflection of real world progression and provides a useful tool for researchers and policy makers. Once completed, this model will provide a tool for evaluation of pressure lowering medications.