Hierarchical Graphical Models: An Application to Pulmonary Function and Cholesterol Levels in the Normative Aging Study

Authors: Zhang, Dabao1; Wells, Martin T.2; Turnbull, Bruce W.3; Sparrow, David4; Cassano, Patricia A.5

Source: Journal of the American Statistical Association, Volume 100, Number 471, September 2005 , pp. 719-727(9)

Publisher: American Statistical Association

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Abstract:

There is continued debate regarding the exact relation between lower cholesterol levels and increased respiratory disease mortality. One of the goals of this study is to reveal the relationship between subcomponents of cholesterol and pulmonary function. We consider the subcomponents of total cholesterol, namely high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, to investigate the relationship of cholesterol levels with pulmonary function in a longitudinal study. To answer these questions, we propose new methodology for hierarchical reciprocal graphical models. We consider the identification and estimation of these models, and propose maximum likelihood estimation using a generalized EM algorithm. A simulation study of the algorithm and the corresponding estimates reveals excellent performance of the proposed procedures. Application of this methodology to the Normative Aging Study reveals complicated associations between pulmonary function and the subcomponents of total cholesterol.

Keywords: EM ALGORITHM; FULL-INFORMATION MAXIMUM LIKELIHOOD ESTIMATION; HIERARCHICAL MODELS; HIGH-DENSITY LIPOPROTEIN; LOWDENSITY LIPOPROTEIN; RANDOM COEFFICIENT; RECIPROCAL GRAPH; TOTAL CHOLESTEROL

Document Type: Research article

DOI: 10.1198/016214505000000114

Affiliations: 1: Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642 2: Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853 3: Operations Research and Industrial Engineering, Cornell University, Ithaca, NY 14853 4: Normative Aging Study, VA Boston Healthcare System and Boston University School of Medicine 5: Nutritional Epidemiology, Cornell University, Ithaca, NY 14853

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