Sparse factors for the positive and negative syndrome scale: Which symptoms and stage of illness?. Issue 3 (28th February 2015)

• Record Type: Journal Article
• Authors: Anderson, Ariana; Wilcox, Marsha; Savitz, Adam; Chung, Hearee; Li, Qingqin; Salvadore, Giacomo; Wang, Dai; Nuamah, Isaac; Riese, Steven P.; Bilder, Robert M.
• Is Part Of: Psychiatry research. Volume 225:Issue 3(2015)
• Page Start: 283
• Page End: 290
• Publication Date: 2015-02-28
• Subjects: PANSS -- Confirmatory factor analysis -- Exploratory factor analysis -- Schizophrenia -- RDoC -- Dimensional Measures; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2014.12.025
• Languages: English
• ISSNs: 0165-1781

Abstract

Abstract: The Positive and Negative Syndrome Scale (PANSS) is frequently described with five latent factors, yet published factor models consistently fail to replicate across samples and related disorders. We hypothesize that (1) a subset of the PANSS, instead of the entire PANSS scale, would produce the most replicable five-factor models across samples, and that (2) the PANSS factor structure may be different depending on the treatment phase, influenced by the responsiveness of the positive symptoms to treatment. Using exploratory factor analysis, confirmatory factor analysis and cross validation on baseline and post-treatment observations from 3647 schizophrenia patients, we show that five-factor models fit best across samples when substantial subsets of the PANSS items are removed. The optimal model at baseline (five factors) omits 12 items: Motor Retardation, Grandiosity, Somatic Concern, Lack of Judgment and Insight, Difficulty in Abstract Thinking, Mannerisms and Posturing, Disturbance of Volition, Preoccupation, Disorientation, Excitement, Guilt Feelings and Depression. The PANSS factor models fit differently before and after patients have been treated. Patients with larger treatment response in positive symptoms have larger variations in factor structure across treatment stage than the less responsive patients. Negative symptom scores better predict the positive symptoms scores after treatment than before treatment. We conclude that sparse factor models replicate better on new samples, and the underlying disease structure of Schizophrenia changes upon treatment. Highlights: The PANSS factor models fit differently before and after patients have been treated. Patients with a larger positive symptom treatment response have larger variations in factor structure than patients whose positive symptoms are less responsive to treatment. After treatment, individual PANSS items are more correlated with each other than before. Glass pore contact angle varies with liquid surface tension and chemical structure. Negative subscales scores are significantly stronger predictors of the positive symptoms after treatment than before treatment; there was a significant interaction effect between treatment and the negative symptom scores in predicting the positive symptom score of a patient. PANSS factor models that removed 40% of the items replicated substantially better than PANSS factor models that retained all 30 items.