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## 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

## Blog

### How to guide on a R Shiny app developed to perform Bayesian inference for diagnostic meta-analysis

Authors: Ian Schiller, Nandini Dendukuri

### Bayesian inference for the bivariate latent class model for diagnostic meta-analysis

Authors: Ian Schiller, Nandini Dendukuri

### Bayesian inference for HSROC diagnostic meta-analysis model

We pursue our series of blog articles illustrating rjags programs for diagnostic meta-analysis with an article describing how to carry out Bayesian inference for the...

### Bayesian inference for the Bivariate model for diagnostic meta-analysis

Contents OVERVIEW MOTIVATING EXAMPLE AND DATA THE MODEL INITIAL VALUES COMPILING AND RUNNING THE MODEL MONITORING CONVERGENCE SUMMARY STATISTICS SROC plot SCRIPT...

### Bayesian Sample Size Calculations for Difference in Proportions

Want to use a Bayesian approach to plan your study for comparing proportions? The R package SampleSizeProportions implements sample size calculations based on...

### Learning rjags using simple statistical models

Here are some examples of fitting simple statistical models in rjags. They are useful for beginners learning Bayesian inference using the rjags library. Notice in...