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Comparative Study
. 2011:2011:435078.
doi: 10.1155/2011/435078. Epub 2011 May 14.

The use of mixed models for the analysis of mediated data with time-dependent predictors

Emily A Blood  1 , Debbie M Cheng
Affiliations
Comparative Study

The use of mixed models for the analysis of mediated data with time-dependent predictors

Emily A Blood et al. J Environ Public Health. 2011.

Abstract

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.

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Figure 1
Path diagram of unrestricted structural equation model.
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References

    1. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963–974. - PubMed
    1. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research. Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986;51(6):1173–1182. - PubMed
    1. Krull JL, MacKinnon DP. Multilevel mediation modeling in group-based intervention studies. Evaluation Review. 1999;23(4):418–444. - PubMed
    1. Bollen KA. Structural Equations with Latent Variables. New York, NY, USA: John Wiley & Sons; 1989.
    1. Muthen B. Best Methods for the Analysis of Change: Recent Advances, Unanswered Questions, Future Directions. Washington, DC, USA: APA; 1991. Analysis of longitudinal data using latent variable models with varying parameters; pp. 1–17.

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