Latent Class Analysis

For many medical conditions (e.g. chlamydia trachomatis infection, tuberculosis in children, pneumonia, Alzheimer’s disease) there is no perfect diagnostic test or measure. This complicates estimation of the prevalence of the condition as well as estimation of the accuracy of diagnostic tests. Latent class models (or finite mixture models) provide a solution for this problem by modeling the observed patterns of test results as if they arise from a mixture of latent, i.e. unobserved, groups with and without the condition of interest.

Here is a link to a presentation on latent class analysis that I gave in 2017: Latent Class Analysis: An Indispensable Method for Diagnostic Accuracy Research And here is a link to a video of me presenting on Estimating diagnostic test accuracy in the absence of a perfect reference: The importance of quantifying uncertainty

From: Dendukuri N, Hadgu A, Wang L. Modeling conditional dependence between diagnostic tests: a multiple latent variable model. Stat Med. 2009 Feb 1;28(3):441-61

My research program has covered different aspects of latent class analysis motivated by problems in diagnostic test accuracy research. Some of my peer-reviewed publications and associated software are listed below:

Adjustment for conditional dependence in latent class models:

Dendukuri N, Joseph L. Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics. 2001 Mar;57(1):158-67.

Dendukuri N, Hadgu A, Wang L. Modeling conditional dependence between diagnostic tests: a multiple latent variable model. Stat Med. 2009 Feb 1;28(3):441-61.

Wang Z, Dendukuri N, Joseph L. Understanding the effects of conditional dependence in research studies involving imperfect diagnostic tests. Stat Med. 2017 Feb 10;36(3):466-480.

Wang Z, Dendukuri N, Zar HJ, Joseph L. Modeling conditional dependence among multiple diagnostic tests. Stat Med. 2017 Dec 30;36(30):4843-4859.

Applications of latent class models:

Schumacher SG, van Smeden M, Dendukuri N, Joseph L, Nicol MP, Pai M, Zar HJ. Diagnostic Test Accuracy in Childhood Pulmonary Tuberculosis: A Bayesian Latent Class Analysis. Am J Epidemiol. 2016 Nov 1;184(9):690-700.

Pai M, Dendukuri N, Wang L, Joshi R, Kalantri S, Rieder HL. Improving the estimation of tuberculosis infection prevalence using T-cell-based assay and mixture models. Int J Tuberc Lung Dis. 2008 Aug;12(8):895-902.

Estimating incremental value in latent class models:

Ling DI, Pai M, Schiller I, Dendukuri N. A Bayesian framework for estimating the incremental value of a diagnostic test in the absence of a gold standard. BMC Med Res Methodol. 2014 May 15;14:67.

Adjustment for verification bias in latent class models:

de Groot JA, Dendukuri N, Janssen KJ, Reitsma JB, Bossuyt PM, Moons KG. Adjusting for differential-verification bias in diagnostic-accuracy studies: a Bayesian approach. Epidemiology. 2011