A pattern recognition matrix for placebo response in Schizophrenia

Objectives: There is a need for real-time capability to detect inconsistencies in efficacy

outcome measures and to predict individual-level or group-level placebo response. Training and

calibration to improve reliability is one method; though this can be undermined by the enrollment of placebo responders.

Methods: A pattern recognition matrix for placebo-response in schizophrenia was developed based on a Phase II study of schizophrenia conducted in the US. A data monitoring algorithm based on the Positive and Negative Syndrome Scale (PANSS) was retrospectively applied to the unblinded data and score patterns for the placebo responders versus placebo non-responders were analyzed.

Results: A total of 35 placebo responders and 35 randomly selected placebo non-responders were compared. For specific patterns during the first 2 visits, patients were significantly likely to be pla-cebo responders. Within this sample of patients who were randomized to placebo, those who dem-onstrated the pattern within the initial study visits were approximately three times more likely (OR=2.9, p=0.027) to demonstrate a placebo response.

Conclusion: This initial finding suggests that this method could aid in the detection of placebo re-sponse early in a trial. In concert with a data-monitoring process we would expect more robust sig-nal detection. The use of the same system, coupled with ongoing training has demonstrated signifi-cant improvements in reliability, increasing the ICC by 19% in a 3-month period. If deployed at startup, this method provides a cost-effective way of managing the data quality in RCTs.