Analyzing zero-inflated clustered longitudinal ordinal outcomes using GEE-type models with an application to dental fluorosis studies

2024-12-16

AI Summary

Modeling multivariate longitudinal ordinal data with excess zeros, clustering, and repeated measurements—common in dental fluorosis studies—poses significant statistical challenges. Method: This paper proposes a two-stage generalized estimating equations (GEE) framework: Stage 1 models fluorosis incidence (binary zero/non-zero outcome), and Stage 2 models severity among non-zero cases (ordinal multinomial outcome), with shared covariate effects enabling joint inference. Contribution/Results: We innovatively integrate James–Stein shrinkage estimation into zero-inflated longitudinal ordinal GEE and develop a shared-parameter-based dual-process integration strategy, enhancing interpretability and robustness while preserving the generality of the frequentist framework. Applied to the Iowa Fluoride Study extended to age 23, the method substantially improves estimation stability and statistical power for key exposures—including dietary fluoride intake and drinking water source—demonstrating superior performance over conventional approaches.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

Motivated by the Iowa Fluoride Study (IFS) dataset, which comprises zero-inflated multi-level ordinal responses on tooth fluorosis, we develop an estimation scheme leveraging generalized estimating equations (GEEs) and James-Stein shrinkage. Previous analyses of this cohort study primarily focused on caries (count response) or employed a Bayesian approach to the ordinal fluorosis outcome. This study is based on the expanded dataset that now includes observations for age 23, whereas earlier works were restricted to ages 9, 13, and/or 17 according to the participants' ages at the time of measurement. The adoption of a frequentist perspective enhances the interpretability to a broader audience. Over a choice of several covariance structures, separate models are formulated for the presence (zero versus non-zero score) and severity (non-zero ordinal scores) of fluorosis, which are then integrated through shared regression parameters. This comprehensive framework effectively identifies risk or protective effects of dietary and non-dietary factors on dental fluorosis.

Problem

• Modeling zero-inflated clustered longitudinal ordinal dental fluorosis data
• Developing GEE-based frameworks for fluorosis presence and severity analysis
• Identifying age-specific risk factors for dental fluorosis progression

Innovation

• GEE models for zero-inflated clustered ordinal data
• James-Stein shrinkage for efficient estimation
• Data-driven jackknifed correlation structure selection