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:

Adjustment for conditional dependence in latent class models:

Applications of latent class models:

Estimating incremental value in latent class models:

Adjustment for verification bias in latent class models:

Sample size calculation for planning studies to apply latent class models:

Latent-class meta-analysis:

Applications of latent-class meta-analysis:

Problems with composite reference standards:


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