%0 Journal Article %T Exploring heterogeneity in clinical trials with latent class analysis %A Zhang, Zhongheng %A Abarda, Abdallah %A Contractor, Ateka A. %A Wang, Juan %A Dayton, C. Mitchell %J Annals of Translational Medicine %D 2018 %B 2018 %9 %! Exploring heterogeneity in clinical trials with latent class analysis %K %X 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. %U https://atm.amegroups.org/article/view/18822 %V 6 %N 7 %P 119 %@ 2305-5847