Correlated Tooth-Level Caries Status in a Type-2 Diabetic Gullah Population Volume 5 - Issue 1

Dipankar Bandyopadhyay¹*, Patrick Hilden², Debdeep Pati³, Jyotika Fernandes⁴, Stephanie L Russell⁵, Jeffrey L Fellows⁶ and Radha Nagarajan⁷

• ¹Department of Biostatistics, Virginia Commonwealth University, Virginia
• ²Department of Biostatistics, Saint Barnabas Medical Center, Livingston, New Jersey
• ³Department of Statistics, Texas A&M University, Texas
• ⁴Department of Medicine, Medical University of South Carolina, South Carolina
• ⁵Department of Epidemiology and Health Promotion, New York University, New York
• ⁶Center for Health Research, Kaiser Permanente, Portland
• ⁷Center for Oral and Systemic Health, Marshfield Clinic Health System, USA

Received: November 1, 2021   Published: November 11, 2021

Corresponding author: Dipankar Bandyopadhyay, Department of Biostatistics, Virginia Commonwealth University, Virginia

DOI: 10.32474/MADOHC.2021.05.000204

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Abstract

Count data abounds in various epidemiological, biological, or clinical settings, and are routinely analyzed using a Poisson or Negative Binomial distribution. The ability to accurately analyze and interpret such data remains an important area of research. In dental caries epidemiology, the DMFT/S index, which is the total number of (D)decayed, (M)issuing and (F)filled (T)teeth, or (S) surfaces, stands out as the single most important marker to quantify overall caries experience. However, while analyzing toothlevel caries responses, we often observe an excess of lower and upper bounded count values, such as 0, and accounting for these excess values remains a key component in selecting the most appropriate biostatistical modeling strategy. The aim of this paper is to compare the effectiveness of Binomial (B), Beta-Binomial (BB), and 2-part Hurdle- Binomial (HB) mixed effects models in analyzing tooth-level dental caries data derived from a clinical study of Type-2 diabetic Gullah speaking Africans residing in the coastal South Carolina (SC) sea-islands. All analyses were conducted using the SAS NLMIXED procedure which provides for flexible model specification allowing for direct fitting of more intricate models such as the BB, 2-part HB model, and other such models for which standard SAS functions do not exist, but for which a likelihood function is known. Our analysis found the BB mixed-effects model to be the most effective. Analysis reveal that for every 1-year increase in age, there is a 4.9% increase (p<0.05) in the mean probability of obtaining an additional DMFS. Furthermore, there is a decrease of 84.6%, 96.9%, and 94.2% (p < 0.05 for all three), respectively for premolars, canines and incisors, in the expected probability of an additional DMFS, compared to the molars. Results from this analysis should provide interesting insights into assessing complex covariate-response relationships in dental caries and should provide some guideline into effective statistical model choices.

Keywords: Caries; Count Data; Tooth-Level, Gullah, SAS Proc NLMIXED

Abstract| Introduction| Materials and Methods| Results| Discussion| Acknowledgements| References|