Project Activities
The research team will develop the statistical analysis by estimating the parameters of the theoretical model via maximum likelihood from incomplete data using adaptive Gauss-Hermite Quadrature. They will extend this approach to multi-site trials in which each can be regarded as possessing a unique set of principal strata.
People and institutions involved
IES program contact(s)
Project contributors
SubAwardee(s)
Products and publications
- HLM with Missing Data: Software for Estimating Hierarchical Models from Incomplete Data
- Available at https://sph.vcu.edu/about/portfolio/details/yshin/
- HGLM with random coefficients, moderation and noncompliance
- Freely download from the publicly accessible GitHub page: https://github.com/sunx63/CACE
- HLM with level‐2 interaction effects of site‐level continuous covariates: Bayesian estimation using a Gibbs sampler
- The implementation code in R is freely available from the publicly accessible GitHub page: https://github.com/shind10/GSExact
Publications:
Shin, D., Shin, Y. and Hagiwara, N. (2025). Bayesian Estimation of Hierarchical Linear Models from Incomplete Data: Cluster‐Level Interaction Effects and Small Sample Sizes, Statistics in Medicine, 44(10-12), e70051, https://doi.org/10.1002/sim.70051.
Shin, Y., & Raudenbush, S. W. (2024). Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error. Journal of Computational and Graphical Statistics, 33(1), 112-125.
Sun, X., Shin, Y., Lafata, J.E., and Raudenbush, S.W. (2024). Variability in Causal Effects and Noncompliance in a Multisite Trial: A Bivariate Hierarchical Generalized Random Coefficients Model for a Binary Outcome. Statistics in Medicine, 43(28), 5353-65, https://doi.org/10.1002/sim.10229.
Questions about this project?
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