# LCA

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):

#### Adjustment for conditional dependence in latent class models:

WinBUGS program for fixed effects model WinBUGS program for random effects model

Below are R Markdown scripts for fixed- and random-effects model.

The random effects model takes advantage of the Gauss-Hermite quadrature approximation to greatly improve on computational speed. Both scripts run with minimal input from the user and will produce an output including parameter estimates, convergence diagnostic tools and more. See the READ ME! file for more details. A dataset (Strongyloides_data) to run with the models is provided to familiarize with the scripts.