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
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 (links to associated programs are provided when available):
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.
MacLean EL, Kohli M, Köppel L, Schiller I, Sharma SK, Pai M, Denkinger CM, Dendukuri N. Bayesian latent class analysis produced diagnostic accuracy estimates that were more interpretable than composite reference standards for extrapulmonary tuberculosis tests. Diagn Progn Res. 2022 Jun 16;6(1):11.
Problems with composite reference standards:
Dendukuri N, Schiller I, de Groot J, Libman M, Moons K, Reitsma J, van Smeden Concerns about composite reference standards in diagnostic research. BMJ. 2018 Jan 18;360:j5779.
Schiller I, van Smeden M, Hadgu A, Libman M, Reitsma JB, Dendukuri N. Bias due to composite reference standards in diagnostic accuracy studies. Stat Med. 2016 Apr 30;35(9):1454-70.
Hadgu A, Dendukuri N, Wang L. Evaluation of screening tests for detecting Chlamydia trachomatis: bias associated with the patient-infected-status algorithm. Epidemiology. 2012 Jan;23(1):72-82.
Hadgu A, Dendukuri N, Hilden J. Evaluation of nucleic acid amplification tests in the absence of a perfect gold-standard test: a review of the statistical and epidemiologic issues. Epidemiology. 2005 Sep;16(5):604-12.