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Quantile regression based method for characterizing risk-specific behavioral patterns in relation to longitudinal
MinJae Lee1,2, Belinda M Reininger3, Kelley Pettee Gabriel4
1Peter O'Donnell Jr. School of Public Health, Department of Health Data Sciences and Biostatistics, University of Texas Southwestern (UTSW), Dallas, TX, USA.
Journal of Applied Statistics
|March 5, 2025
Summary
This study introduces a new statistical method to analyze complex lifestyle behaviors and biomarkers for cancer risk. It helps tailor interventions by understanding individual health patterns and heterogeneity in at-risk populations.
Area of Science:
- Biostatistics
- Epidemiology
- Behavioral Science
Background:
- Lifestyle behaviors significantly influence cancer risk, but assessing them is complex due to multidimensionality and individual heterogeneity.
- Existing methods struggle to link biomarkers with multiple behavioral measurements dynamically, especially with challenges like left-censored biomarker data.
- Tailoring interventions requires accounting for individual differences in behavior and risk.
Purpose of the Study:
- To develop an advanced statistical method for analyzing multidimensional behavioral data in relation to biomarkers.
- To provide a framework for understanding heterogeneous behavioral profiles and their dynamic relation to disease risk.
- To validate the proposed method using simulations and real-world data from Mexican-American adults.
Main Methods:
- Proposed a novel method constructing a quantile-specific weighted index of multiple behavioral measurements.
- Utilized a quantile regression framework to connect biomarker levels with behavioral patterns.
- Addressed challenges in biomarker data analysis, including left-censoring.
Main Results:
- The proposed method provides a multidimensional view of risk-specific behavioral patterns.
- Demonstrated the ability to connect biomarker levels with complex behavioral measurements.
- Successfully illustrated application to the Tu Salud ¡Sí Cuenta! data, examining behavior changes.
Conclusions:
- The developed statistical method effectively addresses the challenges of analyzing heterogeneous, multidimensional behavioral data.
- Offers improved insights into risk-specific behavioral patterns by integrating biomarker information.
- Facilitates tailored public health interventions for at-risk populations by characterizing individual profiles.
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