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

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 datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) 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 RCT were similar despite their distinctive interpretations. 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

A suite of methods for representing activity space in a healthcare accessibility study

BACKGROUND: "Activity space" has been used to examine how people's habitual movements interact with their environment, and can be used to examine accessibility to healthcare opportunities. Traditionally, the standard deviational ellipse (SDE), a Euclidean measure, has been used to represent activity space. We describe the construction and application of the SDE at one and two standard deviations, and three additional network-based measures of activity space using common tools in GIS: the road network buffer (RNB), the 30-minute standard travel time polygon (STT), and the relative travel time polygon (RTT). We compare the theoretical and methodological assumptions of each measure, and evaluate the measures by examining access to primary care services, using data from western North Carolina. RESULTS: Individual accessibility is defined as the availability of healthcare opportunities within that individual's activity space. Access is influenced by the shape and area of an individual's activity space, the spatial distribution of opportunities, and by the spatial structures that constrain and direct movement through space; the shape and area of the activity space is partly a product of how it is conceptualized and measured. Network-derived measures improve upon the SDE by incorporating the spatial structures (roads) that channel movement. The area of the STT is primarily influenced by the location of a respondent's residence within the road network hierarchy, with residents living near primary roads having the largest activity spaces. The RNB was most descriptive of actual opportunities and can be used to examine bypassing. The area of the RTT had the strongest correlation with a healthcare destination being located inside the activity space. CONCLUSION: The availability of geospatial technologies and data create multiple options for representing and operationalizing the construct of activity space. Each approach has its strengths and limitations, and presents a different view of accessibility. While the choice of method ultimately lies in the research question, interpretation of results must consider the interrelated issues of method, representation, and application. Triangulation aids this interpretation and provides a more complete and nuanced understanding of accessibility

Velopharyngeal Status of Stop Consonants and Vowels Produced by Young Children With and Without Repaired Cleft Palate at 12, 14, and 18 Months of Age: A Preliminary Analysis

The objective was to determine velopharyngeal (VP) status of stop consonants and vowels produced by young children with repaired cleft palate (CP) and typically developing (TD) children from 12 to 18 months of age

Accuracy of record linkage software in merging dental administrative data sets: Accuracy of record linkage software

To determine the accuracy of record matching using “Link King” software that uses an ordinal score for the certainty that linked records are valid matches

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined

A new way to estimate disease prevalence from random partial-mouth samples

Standard partial-mouth estimators of chronic periodontitis that define an individual’s disease status solely in terms of selected sites underestimate prevalence. This study proposes an improved prevalence estimator based on randomly sampled sites and evaluates its accuracy in a well characterized population cohort

Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification

BACKGROUND: The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss. METHODS: Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals). RESULTS: The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts. CONCLUSIONS: The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients

Orthogonalized Residuals for Estimation of Marginally Specified Association Parameters in Multivariate Binary Data: Orthogonalized residuals

This paper focuses on marginal regression models for correlated binary responses when estimation of the association structure is of primary interest. A new estimating function approach based on orthogonalized residuals is proposed. A special case of the proposed procedure allows a new representation of the alternating logistic regressions method through marginal residuals. The connections between second-order generalized estimating equations, alternating logistic regressions, pseudo-likelihood and other methods are explored. Eficiency comparisons are presented, with emphasis on variable cluster size and on the role of higher-order assumptions. The new method is illustrated with an analysis of data on impaired pulmonary function