Abstract
We formulate a latent variable model with Bayesian estimation to assess a cluster-level intervention effect on multiple binary outcomes from three-level hierarchical data. This approach incorporates the correlation structure into one latent trait, and simultaneously regresses the latent trait on observed covariates. Random effects are included to model the hierarchical structure. We illustrate use of this single-latent trait model in a 32-site cluster randomized clinical trial of a three-arm intervention to improve the quality of pneumonia care. Simulation studies verify the accuracy of the estimation. This latent variable model provides a comprehensive way to analyze multivariate hierarchical data, by estimating intervention effects with respect to multiple binary outcomes, quantifying relationships among outcomes, identifying those outcomes that are most informative regarding the assumed latent trait, and providing a summary measure of the latent trait (e.g., “quality of care”) at each site.
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Zhao, X., Stone, R.A., Ye, F. et al. A single latent trait model for multiple binary outcomes in a cluster randomized clinical trial. Health Serv Outcomes Res Method 11, 164–178 (2011). https://doi.org/10.1007/s10742-011-0078-2
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DOI: https://doi.org/10.1007/s10742-011-0078-2