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Although hierarchical correlated data are increasingly available andare being used in evidence-based medical practices and health policy decisionmaking, there is a lack of information about the strengths and weaknesses ofthe methods of analysis with such data. In this paper, we describe the use ofhierarchical data in a family study of alcohol abuse conducted in Edmonton, Canada, that attempted to determine whether alcohol abuse in probands isassociated with abuse in their first-degree relatives. We review three methodsof analyzing discrete hierarchical data to account for correlations among therelatives. We conclude that the best analytic choice for typical correlateddiscrete hierarchical data is by nonlinear mixed effects modeling using alikelihood-based approach or multilevel hierarchical modeling using a quasilikelihoodapproach, especially when dealing with heterogeneous patient data.

KEYWORDS

Non-Linear Mixed Effects Model; Multilevel Model; Generalized Estimating Equations; Mantel-Haenszel Odds Ratio; Specificity; Sensitivity

Cite this paper

Y. Liang and K. Carriere -A Comparison of Statistical Methods for Analyzing Discrete Hierarchical Data: A Case Study of Family Data on Alcohol Abuse,- Open Journal of Statistics, Vol. 3 No. 4A, 2013, pp. 1-6. doi: 10.4236-ojs.2013.34A001.





Autor: Yuanyuan Liang, Keumhee Chough Carriere

Fuente: http://www.scirp.org/



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