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.

摘要

回归混合模型 (RMM) 可用于专门测试和模拟异质人群中的差异效应。根据目标 1 模拟研究的结果，使用受限预测器均值进行的计数似乎是有利的。此外，研究人员应该估计K和K+1无条件模型（在初始枚举期间选择），在X上添加C路径，以调查模型不稳定的可能性以及模型被错误指定的可能性，因为基础总体在子组中包含预测变量差异。目标 2 模拟研究探讨了 RMM 对预测变量差异的稳健程度。尽管不同类别的预测变量方差不同的模拟条件的覆盖率不是名义率，但即使存在适度违反此假设的情况下，参数估计也不会产生偏差。