Model selection and prediction of outcomes in recent onset schizophrenia patients who undergo cognitive training

Publication date: March 2018Source: Schizophrenia Research: Cognition, Volume 11Author(s): Ian S. Ramsay, Sisi Ma, Melissa Fisher, Rachel L. Loewy, J. Daniel Ragland, Tara Niendam, Cameron S. Carter, Sophia VinogradovAbstractPredicting treatment outcomes in psychiatric populations remains a challenge, but is increasingly important in the pursuit of personalized medicine. Patients with schizophrenia have deficits in cognition, and targeted cognitive training (TCT) of auditory processing and working memory has been shown to improve some of these impairments; but little is known about the baseline patient characteristics predictive of cognitive improvement. Here we use a model selection and regression approach called least absolute shrinkage and selection operator (LASSO) to examine predictors of cognitive improvement in response to TCT for patients with recent onset schizophrenia. Forty-three individuals with recent onset schizophrenia randomized to undergo TCT were assessed at baseline on measures of cognition, symptoms, functioning, illness duration, and demographic variables. We carried out 10-fold cross-validation of LASSO for model selection and regression. We followed up on these results using linear models for statistical inference. No individual variable was found to correlate with improvement in global cognition using a Pearson correlation approach, and a linear model including all variables was also found not to be significant. However, the LASSO model identified base...

Source: Schizophrenia Research: Cognition - July 10, 2018 Category: Psychiatry Source Type: research