Improving Identification of Tic Disorders in Children

Abstract

ABSTRACT This study combines data from five studies in a quantitative modeling approach to improve identification of tics and tic disorders using two questionnaires (the Motor or Vocal Inventory of Tics and the Description of Tic Symptoms), administered to parents and children (N = 1,307). Combining final diagnoses (positive or negative for tic disorder) with data from recently developed questionnaires implemented to assist in the identification of tics and tic disorders in children, we investigate methods for predicting positive diagnosis while also identifying which items in the questionnaires are most predictive. Logistic regression and random forest models are compared using various summary statistics. We further discuss the differences in errors (false positives versus false negatives) in the specification of predictive model tuning parameters. Compared to logistic regression models, random forest models provided comparable and often superior predictive abilities and were also more useful in summarizing the contributions to predictions from individual questions. The combined analyses identified a subset of screener questions that were the best predictors of tic disorders; the identified questions differed based on parent or self-report. These results provide information to inform the future development of tools to screen for tics in a variety of healthcare and epidemiological settings.