Abstract
It is common to encounter data that have a hierarchical or nested structure. Examples include patients within hospitals within cities, students within classes within schools, factories within industries within states, or families within neighborhoods within census tracts. These structures have become increasingly common in recent times and include variability at each level which must be taken into account. Hierarchical models which account for the variability at each level of the hierarchy, allow for the cluster effects at different levels to be analyzed within the models (Shahian et al. in Ann Thorac Surg, 72(6):2155–2168, 2001). This chapter discusses how the information from different levels can be used to produce a subject-specific model. However, there are often cases when these models do not fit as additional random intercepts and random slopes are added to the model. This addition of additional parameters often leads to non-convergence. We present a simulation study as we explore the cases in these hierarchical models which often lead to non-convergence. We also used the 2011 Bangladesh Demographic and Health Survey data as an illustration.
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Acknowledgements
This work is funded in part by the National Institutes of Health Alzheimer’s Consortium Fellowship Grant, Grant No. NHS0007. The content in this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Irimata, K.M., Wilson, J.R. (2017). Monte-Carlo Simulation in Modeling for Hierarchical Generalized Linear Mixed Models. In: Chen, DG., Chen, J. (eds) Monte-Carlo Simulation-Based Statistical Modeling . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-3307-0_13
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