Tagged: logistic regression

Multilevel Modeling of Periodontal Data (VI)

Triclosan

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.

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Mirrored from Periodontology – Matters arising.

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Multilevel Modeling of Periodontal Data (VI)

TriclosanGraphic 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.

Continue reading…

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.

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29 October 2014 @ 6:17 am.

Last modified Otober 29, 2014.