@article{ATM18822,
author = {Zhongheng Zhang and Abdallah Abarda and Ateka A. Contractor and Juan Wang and C. Mitchell Dayton},
title = {Exploring heterogeneity in clinical trials with latent class analysis},
journal = {Annals of Translational Medicine},
volume = {6},
number = {7},
year = {2018},
keywords = {},
abstract = {Case-mix is common in clinical trials and treatment effect can vary across different subgroups. Conventionally, a subgroup analysis is performed by dividing the overall study population by one or two grouping variables. It is usually impossible to explore complex high-order intersections among confounding variables. Latent class analysis (LCA) provides a framework to identify latent classes by observed manifest variables. Distal clinical outcomes and treatment effect can be different across these classes. This paper provides a step-by-step tutorial on how to perform LCA with R. A simulated dataset is generated to illustrate the process. In the example, the classify-analyze approach is employed to explore the differential treatment effects on distal outcomes across latent classes.},
issn = {2305-5847}, url = {https://atm.amegroups.org/article/view/18822}
}