%0 Journal Article
%T Performance of mixed effects models in the analysis of mediated longitudinal data
%A Emily A Blood
%A Howard Cabral
%A Timothy Heeren
%A Debbie M Cheng
%J BMC Medical Research Methodology
%D 2010
%I BioMed Central
%R 10.1186/1471-2288-10-16
%X We simulated mediated longitudinal data from an SEM where a binary, main independent variable has both direct and indirect effects on a continuous outcome. We conducted analyses with both the LMM and SEM to evaluate the performance of the LMM in a setting where the SEM is expected to be preferable. Models were evaluated with respect to bias, coverage probability and power. Sample size, effect size and error distribution of the simulated data were varied.Both models performed well in a range of settings. Marginal increases in power estimates were observed for the SEM, although generally there were no major differences in performance. Power for both models was good with a sample of size of 250 and a small to medium effect size. Bias did not substantially increase for either model when data were generated from distributions that were both skewed and kurtotic.In settings where the goal is to evaluate the overall effects, the LMM excluding mediating variables appears to have good performance with respect to power, bias and coverage probability relative to the SEM. The major benefit of SEMs is that it simultaneously and efficiently models both the direct and indirect effects of the mediation process.A common method of handling longitudinal data is through linear mixed effects models (LMMs) [1]. These models account for the correlation of observations and allow estimation of the effect of predictor variables on repeated outcomes. They are relatively easy to implement and their regression parameters have a clear interpretability.Complex relationships often exist among the variables studied, however, and it may be of interest to explicitly model the hypothesized causal pathways between independent variables and outcomes. Although multiple mixed effects models can be fit to evaluate mediation (see e.g. Krull and MacKinnon [2] and Baron and Kenny [3]), methods for mediational analyses, such as Structural Equation Models (SEMs), are necessary to simultaneously model mediated re
%U http://www.biomedcentral.com/1471-2288/10/16