Comparison of methods for identifying phenotype subgroups using categorical features data with application to autism spectrum disorder

Abstract

We evaluate the performance of the Dirichlet process mixture (DPM) and the latent class model (LCM) in identifying autism phenotype subgroups based on categorical autism spectrum disorder (ASD) diagnostic features from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text Revision. A simulation study is designed to mimic the diagnostic features in the ASD dataset in order to evaluate the LCM and DPM methods in this context. Likelihood based information criteria and DPM partitioning are used to identify the best fitting models. The Rand statistic is used to compare the performance of the methods in recovering simulated phenotype subgroups. Our results indicate excellent recovery of the simulated subgroup structure for both methods. The LCM performs slightly better than DPM when the correct number of latent subgroups is selected a priori. The DPM method utilizes a maximum a posteriori (MAP) criterion to estimate the number of classes, and yielded results in fair agreement with the LCM method. Comparison of model fit indices in identifying the best fitting LCM showed that adjusted Bayesian information criteria (ABIC) picks the correct number of classes over 90% of the time. Thus, when diagnostic features are categorical and there is some prior information regarding the number of latent classes, LCM in conjunction with ABIC is preferred.