dentistry: Dental X-ray data

In randomLCA: Random Effects Latent Class Analysis

Dental X-ray data

Description

Six dentists evaluated dental x-rays for incipient caries in Handelman et al (1986), data consistss of 5 of the dentists analysed by Espeland and Handelman (1989) using a latent class model. Further analysis incorprating a random effects latent class model by Qu et al (1996), and by Albert and Dodd (2004)

Usage

dentistry

Format

A data frame with 32 observations on the following 6 variables.

V1

Dentist 1

V2

Dentist 2

V3

Dentist 3

V4

Dentist 4

V5

Dentist 5

freq

Number of subjects

Source

Espeland and Handelman (1989)

References

Handelman, S.L., Leverett, D.H., Espeland, M.A. and Curzon, J.A. (1986) Clinical radiographic evaluation of sealed carious and sound tooth surfaces. Journal of the American Dental Association, 113, 751–754.

Espeland, M.A. and Handelman, S.L. (1989) Using latent class models to characterize and assess relative error in discrete distributions. Biometrics, 45, 587–599.

Qu, Y., Tan, M. and Kutner, M.H. (1996) Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics, 52, 797–810.

Albert P.S. and Dodd, L.E. (2004) A cautionary note on the robustness of Latent Class Models for estimating diagnostic error without a gold standard. Biometrics, 60, 427–435.

Examples

# fit LCR model from Qu et al (1996)
dentistry.lca <- randomLCA(dentistry[, 1:5], freq = dentistry$freq, cores = 1)
# start with constant laoding
dentistry.lcarandom <- randomLCA(dentistry[, 1:5], freq = dentistry$freq, 
	random = TRUE, probit = TRUE, cores = 1)
# allow loading to vary by dentist
dentistry.lcarandomunequal <- randomLCA(dentistry[, 1:5], freq = dentistry$freq, 
	random = TRUE, constload = FALSE, probit = TRUE, cores = 1)

randomLCA documentation built on July 9, 2023, 6:09 p.m.