title: The use of mixed models for the analysis of mediated data with time-dependent predictors.
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.
authors.affiliation: Children's Hospital Boston and Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. emily.blood@childrens.harvard.edu
authors.forename: Emily A
authors.initials: EA
authors.lastname: Blood
authors.forename: Debbie M
authors.initials: DM
authors.lastname: Cheng
mesh_terms: D000428:Alcohol Drinking / Q000009:adverse effects; D044966:Anti-Retroviral Agents / Q000627:therapeutic use; D015982:Bias; D018791:CD4 Lymphocyte Count; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D015658:HIV Infections / Q000453:epidemiology* / Q000276:immunology; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D011446:Prospective Studies; D013997:Time Factors
chemical_list: D044966:Anti-Retroviral Agents