Multilevel Modeling of Periodontal Data (II)

For those who are interested, here comes chapter 2 of my manual for MLwiN 2.30 using exclusively own periodontal data. As mentioned earlier, this is still work in progress and any comments are welcome.



Manual version 2014.1

Hans-Peter Müller


2 Variance Components 

In the previous chapter, the question had been asked whether gingival thickness, as measured mid-buccally at each tooth, was related to width of buccal gingiva. Several models had been built in a stepwise approach: a model ignoring the subject level, a two-level random intercept model, and a two-level random coefficient model. It could be shown that each model fitted the collected data better than the previous one. Eventually it turned out that the influence of gingival width on gingival thickness, if any, was low in general but depended significantly on the subject.

Gingival dimensions, i.e. its width and thickness, have long been related to the so-called periodontal phenotype, which appears to be a characteristic of a given subject. Gingival dimensions show great intra- and interindividual variation which is associated with tooth type and shape, and which is also mainly genetically determined. Our group has used cluster analysis of gingival appearance at upper anterior teeth and the respective shapes of the teeth of data collected in two independent samples of young adults (Müller and Eger 1997, Müller et al. 2000b) to study the periodontal phenotype in some detail. As cluster analysis  is largely explorative, and external validity is questionable, the typical hierarchical structure of data collected in a third sample acquired in dental students was analyzed by multilevel modeling (Müller and Könönen 2005) with the specific aim to determine subject variation of gingival thickness, a supposed important part of the periodontal phenotype.

A most reasonable way to set up any multilevel model is to start with the basic variance components, or null, model without any covariates. The model should then be build up to increasing complexity by adding possible covariates which are assumed to have an influence on the response variable, and then checking whether they have substantial and/or significant fixed and random coefficients.

Continue reading

2 May 2014 @ 5:18 pm.

Last modified May 2, 2014.


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