Measurement error correction in food substitution analysis
The precision of food substitution analysis is heavily dependent on the accuracy of dietary intake measurements. Commonly used dietary assessment methods, such as food frequency questionnaires (FFQs) and 24-hour dietary recalls, are prone to measurement errors, including misreporting and recall bias. These errors can introduce significant distortions in the results of substitution models, leading to misleading conclusions about the health effects of dietary changes. Furthermore, the complexity and interrelated nature of diets add another layer of difficulty. When one food is substituted for another, it often results in changes across the entire diet, making it challenging to isolate the effects of a single substitution without considering the broader dietary context.
One promising approach to address these challenges is the application of compositional data analysis (CoDA). Compositional data refers to datasets where the components, such as different food types, represent parts of a whole and are expressed in relative terms, such as proportions of total food weight or calorie intake. This approach respects the inherent sum-to-one constraint in dietary data, where all nutrient intakes must sum to a total (e.g., 100% of the diet). However, modeling measurement errors in compositional data presents its own set of challenges. These errors are intertwined with multivariate nutrient intakes and must satisfy the sum-to-one constraint, making it difficult to isolate and correct them using traditional methods.
The overarching goal of this project is to develop methods for correcting measurement errors in food substitution analysis to provide valid statistical inferences. By adopting a compositional data perspective, we aim to model measurement errors within the framework of multivariate compositional data. Additionally, we will extend this framework to incorporate longitudinal data, integrating dietary data with physical activity and clinical biomarker data. This comprehensive approach will allow us to model the combined effects of lifestyle factors on cancer risk more accurately, ultimately leading to more reliable dietary recommendations for disease prevention and health promotion.
Specific Aim 1: Develop a Robust Statistical Framework for Correcting Measurement Errors in Compositional Food Substitution Analysis
The first aim of this project is to develop a novel statistical framework to correct for measurement errors in food substitution analysis, specifically within the context of compositional data. Given the inherent sum-to-one constraint in dietary data, traditional methods are inadequate for addressing the complexities of measurement errors in this setting. We will design and implement a compositional data model that accurately captures the interdependencies of nutrient intakes while correcting for both random and systematic errors. This model will be validated using simulated and real-world dietary datasets, ensuring its applicability and reliability in nutritional epidemiology.
Specific Aim 2: Extend the Compositional Error-Correction Model to Longitudinal Dietary Data
The second aim is to extend the compositional error-correction model to longitudinal dietary data. Diets are not static; they evolve over time, and this dynamic nature must be captured to understand the long-term effects of food substitution on health outcomes. We will develop methods that account for temporal changes in dietary patterns and measurement errors over multiple time points. This extended model will allow for more accurate and comprehensive analyses of how dietary substitutions impact health across different stages of life, providing insights into the timing and duration of dietary changes needed for optimal health benefits.
Specific Aim 3: Integrate Dietary Data with Physical Activity and Clinical Biomarkers to Model Lifestyle Effects on Cancer Risk
The third aim is to integrate the corrected compositional dietary data with other key lifestyle factors, such as physical activity and clinical biomarkers, to model their combined effects on cancer risk. By linking dietary patterns with physical activity levels and biomarkers (e.g., blood glucose, cholesterol levels), we will create a comprehensive model that reflects the complex interplay between diet, lifestyle, and health. This integrative approach will enable us to evaluate the effects of specific food substitutions within the broader context of an individual’s lifestyle, leading to more personalized and effective dietary recommendations for cancer prevention and other health outcomes.
Grace Hong