Transformed PANSS Factors Intended to Reduce Pseudospecificity Among Symptom Domains and Enhance Understanding of Symptom Change in Antipsychotic-Treated Patients With Schizophrenia

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.