Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models

Regression mixture models (RMMs) can be used to specifically test for and model differential effects in heterogeneous populations. Based on the results of the Aim 1 simulation study, enumeration conducted with constrained predictor means appears to be advantageous. Furthermore, researchers should estimate the K and K+1 unconditional models (chosen during initial enumeration), adding the C on X paths, to investigate the potential for model instability as well as the possibility that the models are misspecified because the underlying populations contain predictor variance differences in the subgroups. The Aim 2 simulation study explored the extent to which RMMs are robust to predictor variance differences. Although the coverage rates for the simulation conditions where the predictor variances differed across classes were not the nominal rate, parameter estimates were not biased even in the presence of moderate violations of this assumption.

Automated summary

Regression mixture models can be used to test and model differential effects in heterogeneous populations, with constrained predictor means enumeration appearing advantageous. Researchers should estimate the K and K+1 unconditional models, adding the C on X paths to investigate model instability and potential misspecification, while the Aim 2 simulation study found that RMMs are robust to predictor variance differences, even in the presence of moderate violations of assumptions.