🤖 AI Summary
This study addresses the challenge of latent class modeling for mixed continuous and binary data by proposing a unified joint-likelihood framework that models continuous variables with normal distributions and binary variables with Bernoulli distributions. The latent class structure is efficiently estimated via the EM algorithm. A key contribution is the development of the first frequentist R package tailored to such mixed-data latent class models, which accommodates heteroscedasticity and censoring, eliminates the need for manual likelihood derivation by users, and includes dedicated tools for summarization and visualization. The method has been successfully applied to EQ-5D-5L value set estimation, demonstrating its practical utility and ease of use in health economics and related fields.
📝 Abstract
The R package hyreg2 introduces a frequentist framework for estimating latent class models for mixed outcome types using a joint likelihood approach. The method combines continuous and dichotomous data under the assumption that both outcome types arise from a common underlying data-generating process. In the implemented model, continuous responses are assumed to follow a normal distribution, while dichotomous responses are modeled using a binomial distribution. Such models are used in various scientific disciplines to estimate a common set of parameters across different types of data (e.g. clinical trials, econometrics and health economics). Latent class estimation is performed using the expectation-maximization algorithm as implemented in the widely used R package flexmix. The hyreg2 package offers a user-friendly implementation of this joint likelihood framework, allowing users to estimate models without explicitly programming the likelihood function. Heteroskedasticity as well as censored data can be taken into account. In addition to model estimation, the package provides dedicated summary and visualization functions to facilitate the interpretation of results. The article presents the methodological framework underlying the package and illustrates its functionality through an example based on the estimation of an EQ-5D-5L value set.