Short-term efficacy reliably predicts long-term clinical benefit in rheumatoid arthritis clinical trials as demonstrated by model-based meta-analysis.

The objective of this study was to assess the relationship between short-term and long-term treatment effects measured by the American College of Rheumatology (ACR) 50 responses and to assess the feasibility of predicting 6-month efficacy from short-term data. A rheumatoid arthritis (RA) database was constructed from 68 reported trials. We focused on the relationship between 3- and 6-month ACR50 treatment effects and developed a generalized nonlinear model to quantify the relationship and test the impact of covariates. The Delta ACR50 at 6 months strongly correlated with that at 3 months, moderately correlated with that at 2 months, and only weakly correlated with results obtained at <2 months. A scaling factor that reflected the ratio of 6- to 3-month treatment effects was estimated to be 0.997, suggesting that the treatment effects at 3 months are approaching a "plateau." Drug classes, baseline Disease Activity Score in 28 Joints, and the magnitude of control arm response did not show significant impacts on the scaling factor. This work quantitatively supports the empirical clinical development paradigm of using 3-month efficacy data to predict long-term efficacy and to inform the probability of clinical success based on early efficacy readout.