Bias-adjusted three-step latent class analysis using R and the gsem command in Stata

This method is often performed using MPLUS, LATENT GOLD, or specific functions in Stata. Here we will describe a novel means to perform this analysis using the poLCA package in R to perform the first two steps and the gsem command in Stata to perform the third step. This methodology is applied to a case study involving performing causal analysis by integrating inverse probability of treatment weights into the methodology. We will also demonstrate how to obtain estimates of the average causal effect of exposure on a latent class using the margins command with robust standard errors. Our aim is to broaden awareness of three-step latent class methods and causal analysis and offer means to perform this methodology for users of R, for which there currently is little software available.