We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected...

In latent class analysis (LCA) one seeks a clustering of categorical data, such as patterns of symptoms of a patient, in terms of locally independent stochastic models. This leads to practical definitions of criteria, e.g., whether to include patients in further diagnostic examinations. The clustering is often determined by parameters that are estimated by the maximum likelihood method. The lik...

conclusions fib-4 confirmed the best value for diagnosis of significant fibrosis. apri had a sub-optimal diagnosis accuracy for significant fibrosis. lsm showed the most balance diagnosis value for cirrhosis with the highest sensitivity and moderate specificity. patients and methods a total of 544 patients with chb were assessed for fibrosis stage by four noninvasive tests containing liver stif...