Statistical Analysis of Dental Caries: Different Methods for Different Outcomes

Long, D. Leann; Preisser, John S.; Stamm, John W.

doi:10.1159/000339374

Caries Res

2012

DOI: 10.1159/000339374

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Statistical Analysis of Dental Caries: Different Methods for Different Outcomes

D. Leann Long¹,

John S. Preisser²,

John W. Stamm

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2017

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“…The second data set is from a three-year double-blind caries incidence trial in Lanarkshire, Scotland conducted from 1988 to 1992 that randomized 4,294 children aged 11–12 years to three active toothpaste formulations to compare their respective anticaries efficacy [Stephen et al, 1994]. The different interpretations provided by the models are emphasized to support the assertion that drawing valid conclusions in caries research relies on matching the statistical analysis to a well-articulated research question [Long et al, 2012]. …”

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Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

2017

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Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts.

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Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

2017

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Statistical Analysis of Dental Caries: Different Methods for Different Outcomes

Cited by 1 publication

References 5 publications

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

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