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• W3133669845 abstract "This paper demonstrates the utility of latent classes in evaluating the effect of an intervention on an outcome through multiple indicators of mediation. These indicators are observed intermediate variables that identify an underlying latent class mediator, with each class representing a different mediating pathway. The use of a latent class mediator allows us to avoid modeling the complex interactions between the multiple indicators and ensures the decomposition of the total mediating effects into additive effects from individual mediating pathways, a desirable feature for evaluating multiple indicators of mediation. This method is suitable when the goal is to estimate the total mediating effects that can be decomposed into the additive effects of distinct mediating pathways. Each indicator may be involved in multiple mediation pathways and at the same time multiple indicators may contribute to a single mediating pathway. The relative importance of each pathway may vary across subjects. We applied this method to the analysis of the first 6 months of data from a 2-year clustered randomized trial for adults in their first episode of schizophrenia. Four indicators of mediation are considered: individual resiliency training; family psychoeducation; supported education and employment; and a structural assessment for medication. The improvement in symptoms was found to be mediated by the latent class mediator derived from these four service indicators. Simulation studies were conducted to assess the performance of the proposed model and showed that the simultaneous estimation through the maximum likelihood yielded little bias when the entropy of the indicators was high." @default.
• W3133669845 created "2021-03-15" @default.
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• W3133669845 date "2021-03-09" @default.
• W3133669845 modified "2023-10-16" @default.
• W3133669845 title "Latent class mediator for multiple indicators of mediation" @default.
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• W3133669845 doi "https://doi.org/10.1002/sim.8929" @default.
• W3133669845 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8187142" @default.
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• W3133669845 hasPublicationYear "2021" @default.
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