A unified approach for evaluating the prediction of treatment effect across different types of endpoints.

Phase 2 and 3 development failure is one of the key factors for high drug development cost. Robust prediction of a candidate drug's efficacy and safety profile could potentially improve the success rate of the drug development. Therefore, systematic evaluation of the prediction is important for learning and continuous improvement of the prediction. In this article, we proposed a set of unified criteria that allow to evaluate the predictions across different endpoints, indications and development stages: standardized bias (SB), standardized mean squared errors (SMSE), and credibility of prediction. We applied the SB and SMSE to the predicted treatment effects for 54 comparisons in 5 compounds in immunology and diabetes.