PMID: 15381718Sep 24, 2004Paper

Modeling multivariate binary responses with multiple levels of nesting based on alternating logistic regressions: an application to caries aggregation

Journal of Dental Research
Cande V Ananth, M L Kantor

Abstract

Clustered binary responses are commonly encountered in dental research. Data analysis may include modeling both the marginal response probabilities (i.e., risk) and the dependence structure between pairs of responses (i.e., aggregation). While second-order generalized estimating equations (GEE2) is a well-known approach for such data, alternating logistic regressions (ALR) is a computationally efficient alternative method, especially for large clusters. We illustrate ALR with an application to caries aggregation using a dataset with 3 levels of nesting: tooth surfaces within an interproximal (IP) region, IP regions within a jaw, and jaws within a subject. Caries lesions appear to aggregate strongly within subjects with a spatially distributed risk. The minimum within-IP-region odds ratio (OR) was 2.25 (95% confidence interval 1.15, 4.41), and the within-IP-region ORs were always greater than the between-IP-region ORs. ALR is a convenient and useful regression technique for explicit modeling of the dependence structure, and may be applicable to other dental research problems involving clustered or nested responses.

Citations

Jan 1, 2011·Journal of the American Statistical Association·Ori Davidov, Shyamal Peddada
Oct 30, 2010·Diabetes Research and Clinical Practice·Hon K YuenSteven London
Apr 13, 2010·Archives of Oral Biology·Solaiman AfroughiMehdi Ghandehari Motlagh
Mar 7, 2007·Statistics in Medicine·G BurnsideP R Williamson
Jul 1, 2012·Journal of the Royal Statistical Society. Series A, (Statistics in Society)·Beth A ReboussinMark Wolfson
May 29, 2010·Journal of Clinical Periodontology·Dipankar BandyopadhyayRenata S Leite

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