# Tagged: MLwiN

# Multilevel Modeling of Periodontal Data (VII)

**Update below.**

**7 Ordered Categorical Response Models**

In several chapters so far we have addressed modeling a binary response variable, for instance gingival bleeding upon periodontal probing, which has only two expressions, yes or no (or 1, 0). Many kinds of response variables are ordered categorical. For instance, the Silness & Löe plaque index, which assesses the amount of supragingival plaque at a certain tooth surface adjacent to the gingival margin, comprises clinically defined situations on a scale of scores from 0 to 3 (*Table 7.1*).

6 January 2015, 11:12 am.

Last modified January 6, 2015.

**Update.**

My *MLwiN* user manual using periodontal data has been completed but may be supplemented with further models if applicable.

# Multilevel Modeling of Periodontal Data (VI)

Graphic display of results of a randomized clinical trial may lead to interesting hypotheses which might be tested with more sophisticated statistical methods. In a steady-state plaque environment, the topographical distribution of plaque, as assessed by Silness & Löe’s plaque index, in subjects using triclosan-containing test toothpaste for 6 weeks (after a 4 week preparatory phase) or fluoride control toothpaste is shown in the upper two panels. Results in IL-1 genotype-positive and -negative subjects are differentiated. While plaque distribution was largely comparable, consistent (mean plaque index scores of each examination were displayed on top of each other) and symmetric and followed a distinct pattern; bleeding on probing (a binary response), which is shown in the lower two panels, was not consistent, not symmetric and did not really follow a distinct pattern. Prevalence of bleeding on probing in the two most right panels (Il-1 genotype positive test toothpaste users) seems to differ from the other groups. The procedures described in the following chapter of my manual may indeed allow modeling of site-specific bleeding on probing.

**6 Repeated Measures Models for Binary Outcomes**

In Chapter 3, we had described simple, and quite complex, repeated measures time series models in which continuous outcomes, for instance, gingival thickness or gingival recession, were modeled over time after the implantation of a bio-resorbable membrane, when it had to be assumed that the responses were non-linear and non-monotonic.

In this chapter we want to model the binary outcome, bleeding on gingival probing, in subjects with mild plaque-induced gingival disease over time. While participants of the 1999 Workshop on Periodontal Diseases and Conditions had realized that most gingival inflammation is indeed dental plaque-induced, there seem to be numerous intrinsic and extrinsic factors which may modify the response. For instance, a common toothpaste compound, Triclosan, seems to dampen gingival inflammation in the presence of dental plaque (Müller et al. 2006). One may also ask whether the so-called interleukin-1 genotype, a combination of two single polymorphisms in the IL-1 gene, i.e. a haplotype, which had been associated with increased susceptibility for destructive periodontal disease (Kornman et al.1997) has a clinically discernable influence on the inflammatory response on dental plaque.

Mirrored from Periodontology – Matters arising.

# Multilevel Modeling of Periodontal Data (VI)

Graphic display of results of a randomized clinical trial may lead to interesting hypotheses which might be tested with more sophisticated statistical methods. In a steady-state plaque environment, the topographical distribution of plaque, as assessed by Silness & Löe’s plaque index, in subjects using triclosan-containing test toothpaste for 6 weeks (after a 4 week preparatory phase) or fluoride control toothpaste is shown in the upper two panels. Results in IL-1 genotype-positive and -negative subjects are differentiated. While plaque distribution was largely comparable, consistent (mean plaque index scores of each examination were displayed on top of each other) and symmetric and followed a distinct pattern; bleeding on probing (a binary response), which is shown in the lower two panels, was not consistent, not symmetric and did not really follow a distinct pattern. Prevalence of bleeding on probing in the two most right panels (Il-1 genotype positive test toothpaste users) seems to differ from the other groups. The procedures described in the following chapter of my manual may indeed allow modeling of site-specific bleeding on probing.

**6 Repeated Measures Models for Binary Outcomes**

In Chapter 3, we had described simple, and quite complex, repeated measures time series models in which continuous outcomes, for instance, gingival thickness or gingival recession, were modeled over time after the implantation of a bio-resorbable membrane, when it had to be assumed that the responses were non-linear and non-monotonic.

In this chapter we want to model the binary outcome, bleeding on gingival probing, in subjects with mild plaque-induced gingival disease over time. While participants of the 1999 Workshop on Periodontal Diseases and Conditions had realized that most gingival inflammation is indeed dental plaque-induced, there seem to be numerous intrinsic and extrinsic factors which may modify the response. For instance, a common toothpaste compound, Triclosan, seems to dampen gingival inflammation in the presence of dental plaque (Müller et al. 2006). One may also ask whether the so-called interleukin-1 genotype, a combination of two single polymorphisms in the IL-1 gene, i.e. a haplotype, which had been associated with increased susceptibility for destructive periodontal disease (Kornman et al.1997) has a clinically discernable influence on the inflammatory response on dental plaque.

22 December 2014 @ 1:10 pm.

Last modified December 22, 2014.

# Multilevel Modeling of Periodontal Data (V)

**5 Logistic Models for Binary and Binomial Responses**

In the previous chapters, continuous response variables had been considered in various variance components, random intercept, random coefficient, time series, and multivariate time series multilevel models. In this chapter, we want to look at binary or binomial (proportion) responses. We will mainly focus on the logit link function. As usual, we start with a single-level model and extend this to appropriately consider the three-level hierarchical structure. We also explore contextual effects here. Significance testing and model interpretation using odds ratios and variance partition coefficients are discussed.

** **

**5.1 Description of the Example Data Set**

The data for an example are stored in an EXCEL file (*bop_pli01.xlsx*). The binary response variable here is presence or absence of bleeding on probing (BOP) at gingival units in 50 students at Kuwait University. All had plaque-induced gingival disease.

29 October 2014 @ 6:17 am.

Last modified Otober 29, 2014.

# Multilevel Modeling of Periodontal Data (III)

After a long hiatus due to other projects, final exams and, well, some vacation, the next chapter of my multilevel modeling manual was overdue, but here it comes.

**MULTILEVEL MODELING OF PERIODONTAL DATA**

Manual version 2014.1

*Hans-Peter Müller*

**3 Time Series Models**

In the two previous chapters, observations have been modeled which had been made at teeth which were nested in subjects. Now, consider that observations had been repeated in a longitudinal way, for example after therapy. For that purpose, we want to define a lower level (one) as repeated measures, or occasion, while teeth and subjects would define levels two and three, respectively.

Consider, for instance changes of the entire gingival unit after the implantation of a bio-resorbable membrane for surgical root coverage employing the principle of guided tissue regeneration. Apart from achievable root coverage, the implantation of the membrane and concomitant coronal advancement of the mucoperiosteal flap leads to an immediate increase in thickness of gingiva and later in width of keratinized tissue. Of course, the mucogingival border is displaced but may re-establish itself later in its original position. Note that these alterations at teeth are non-linear and, since wound healing proceeds and regenerated tissue matures over time, non-monotonic (see example (C) in Fig. 13.1 in Rasbash et al. 2012). And they certainly depend on the subject. So, we have a three-level structure with a sample of subjects in which we study gingival dimensions at teeth, which have been surgically treated, over time. The data had been analyzed by multilevel modeling in some detail by Müller (2008).

In this chapter we want to introduce the data set which had been created in a longitudinal study (Müller et al. 2000d) and set up several multilevel models further elaborating the basic time series model.

**3.1 Repeated Measures Data on Gingival Dimensions after Surgical Root Coverage**

The data set consists of observations which have been made in 14 patients who had presented with a large variety of recession types at altogether 31 teeth. They had been treated according to the principles of guided tissue regeneration employing a bio-resorbable membrane. Surgical root coverage consisted, after periosteal dissection, of a coronally advanced flap which was secured with sling sutures. Several preoperative clinical parameters and intrasurgical observations were assessed. Patients were followed up for 1 year, and re-examinations of the clinical situation were carried out after 3, 6, 9 and 12 months.

14 August 2014 @ 8:01 am.

Last modified August 14, 2014.