Item selection by Latent Class-based methods

TL;DR

This paper applies a latent class-based item selection algorithm to nursing home quality data, improving clustering accuracy by identifying relevant questionnaire items and determining the optimal number of classes.

Contribution

It demonstrates the use of an item selection method for latent class models on real healthcare data, enhancing clustering and model selection accuracy.

Findings

Selected items improved clustering quality.

Optimal number of classes was identified.

Validation confirmed robustness of results.

Abstract

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items. In such a context, the Latent Class (LC) model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to real data collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis to different specifications of the initial subset of items and of a resampling procedure.