No abstract

Positive and Negative Syndrome Scale (PANSS) total score is the standard primary efficacy measure in acute treatment studies of schizophrenia. However, PANSS factors that have been derived from factor analytic approaches over the past several decades have uncertain clinical and regulatory status as they are, to varying degrees, intercorrelated. As a consequence of cross-factor correlations, the apparent improvement in key clinical domains (eg, negative symptoms, disorganized thinking/behavior) may largely be attributable to improvement in a related clinical domain, such as positive symptoms, a problem often referred to as pseudospecificity. Here, we analyzed correlations among PANSS items, at baseline and change post-baseline, in a pooled sample of 5 placebo-controlled clinical trials (N = 1710 patients), using clustering and factor analysis to identify an uncorrelated PANSS score matrix (UPSM) that minimized the degree of correlation between each resulting transformed PANSS factor. The transformed PANSS factors corresponded well with discrete symptom domains described by prior factor analyses, but between-factor change-scores correlations were markedly lower. We then used the UPSM to transform PANSS in data from 4657 unique schizophrenia patients included in 12 additional lurasidone clinical trials. The results confirmed that transformed PANSS factors retained a high degree of specificity, thus validating that low between-factor correlations are a reliable property of the USPM when transforming PANSS data from a variety of clinical trial data sets. These results provide a more robust understanding of the structure of symptom change in schizophrenia and suggest a means to evaluate the specificity of antipsychotic treatment effects.

Background and Objective Current methods are not designed to relate the incidence of individual adverse events reported in clinical trials to the plurality of adverse events accumulated in spontaneous reporting databases during real-world use. We have previously reported on a pharmacological class-effect query of clinical trial data defined by a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) post-marketing data. The aim of the current analysis was to apply a dopamine D 2 -based pharmacological class-effect query to clinical trial safety data of an atypical antipsychotic tested across different patient populations. Methods Patient-level adverse event data (n = 4400) from controlled clinical trials of the antipsychotic risperidone in schizophrenia, bipolar disorder, Alzheimer's disease psychosis, and autism were obtained through the Yale University Open Data Access (YODA) project. An Empirical Bayes Geometric Mean analysis was performed, and a three-fold threshold incidence level was applied to determine if a preferred term met criteria for being an antipsychotic class-related adverse event.Results In pooled data from seven trials of adult schizophrenia, class-specific adverse events were identified in 49% of patients treated with risperidone; in 49% of risperidone-treated patients in two trials in adolescent schizophrenia; in 65% of risperidone-treated patients in four trials in adult bipolar disorder; in 50% of risperidone-treated patients in two trials in adolescent schizophrenia; in 36% of risperidone-treated patients in one trial in Alzheimer's disease; and in 94% of risperidonetreated patients in one trial in autism. Conclusions The cumulative curves of class-specific adverse events in risperidone clinical trials of schizophrenia were similar to those first reported for other atypical antipsychotic drugs. However, the class-specific adverse event curves were notably lower for Alzheimer's disease and higher for autism, suggesting that the diagnostic indication may have an important effect on the cumulative class-specific side-effect burden.