Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to…

• NI
  National Institutes of Health
• UO
  University of North Carolina at Chapel Hill
• NI
  National Institute on Drug Abuse
• EK
  Eunice Kennedy Shriver National Institute of Child Health and Human Development