Research Techniques Made Simple: Latent Class Analysis.

Naldi, Luigi; Cazzaniga, Simone (2020). Research Techniques Made Simple: Latent Class Analysis. Journal of investigative dermatology, 140(9), 1676-1680.e1. Elsevier 10.1016/j.jid.2020.05.079

[img] Text
Cazzaniga_2.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.
Author holds Copyright

Download (302kB) | Request a copy

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Dermatology, Urology, Rheumatology, Nephrology, Osteoporosis (DURN) > Clinic of Dermatology

UniBE Contributor:

Cazzaniga, Simone

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0022-202X

Publisher:

Elsevier

Language:

English

Submitter:

Sandra Nyffenegger

Date Deposited:

20 Jan 2021 15:54

Last Modified:

20 Jan 2021 15:54

Publisher DOI:

10.1016/j.jid.2020.05.079

PubMed ID:

32800180

BORIS DOI:

10.48350/150442

URI:

https://boris.unibe.ch/id/eprint/150442

Actions (login required)

Edit item Edit item
Provide Feedback