Applying Blinded Data Analytics to Better Understand Drug-Placebo Separation in Acute Schizophrenia Clinical Trials

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Since the introduction of the first treatment for Schizophrenia, subsequent development of new and more effective antipsychotics has been slow. One trend contributing to the difficulty detecting signal in clinical trials, increasing placebo response and declining treatment effect have been reported for new drug applications submitted to the FDA for several decades.

This is attributed to several well-publicized factors, but a new approach in which data analytics were applied retroactively to acute schizophrenia clinical trials has revealed previously unexplored patterns. The research, conducted by Signant Health and Karuna Therapeutics, highlighted a correlation between six markers of aberrant data variability and drug and placebo response.

In this article, “The Impact of Aberrant Data Variability on Drug–Placebo Separation and Drug/Placebo Response in an Acute Schizophrenia Clinical Trial”, the authors outline how the presence of these data variability indicators can predict robust placebo response and  diminished drug-placebo separation. At then end, look for their recommendations for interventions to minimize risks to endpoint data quality.

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