Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models

ABSTRACT

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.

KEYWORDS:

• Regression
• mixture
• latent
• heterogeneity

Acknowledgments

The authors wish to thank our ECHO colleagues from the Family Life Project (ECHO Award Number UH3OD023332; Blair C [MPI], Swingler MM [site PI], and Gatzke–Kopp LM [site PI]) and the Early Growth and Development Study (ECHO Award Number UH3OD023389; Leve LD [MPI], Ganiban JM [MPI], and Neiderhiser JM [MPI]) who contributed data for the applied example in this study. We wish to thank the medical, nursing and program staff, as well as the children and families participating in the ECHO cohorts. We acknowledge the contribution of the following ECHO program collaborators: ECHO Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith PB, Newby KL, Benjamin DK. We also wish to thank the ECHO Data Analysis Center: Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland: Jacobson LP; Research Triangle Institute, Durham, North Carolina: Parker CB.

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Additional information

Funding

Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) program, Office of The Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023319 (Cella & Gershon, MPIs, Sherlock, co-I). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.