Research Techniques Made Simple
Research Techniques Made Simple: Latent Class Analysis

https://doi.org/10.1016/j.jid.2020.05.079Get rights and content
Under an Elsevier user license
open archive

Latent class analysis (LCA) is a statistical technique that allows for identification, in a population characterized by a set of predefined features, of hidden clusters or classes, that is, subgroups that have a given probability of occurrence and are characterized by a specific and predictable combination of the analyzed features. Compared with other methods of so called data segmentation, such as hierarchical clustering, LCA derives clusters using a formal probabilistic approach and can be used in conjunction with multivariate methods to estimate parameters. The optimal number of classes is the one that minimizes the degree of relationship among cases belonging to different classes, and it is decided by relying on methods such as the Bayesian Information Criterion that capitalize on the value of the negative log-likelihood function, a well-established measure of the goodness of fit of a statistical model. LCA has not been extensively used in dermatology. The areas of application are manifold, from the phenotype classification to the analysis of behavior in relation with risk factors to the performance of diagnostic tests.

Abbreviations

AIC
Akaike Information Criterion
BIC
Bayesian Information Criterion
FMM
finite mixture model
HS
hidradenitis suppurativa
LC
latent class
LCA
latent class analysis
RF
rheumatoid factor
RMLCA
repeated measures latent class analysis

Cited by (0)