Statistical approaches to chronic disease preventive behaviors profiling

Music City Center 

Promoting positive lifestyle behaviors to attenuate lifetime risk of cancers and related chronic diseases is of great interest to public health researchers. Given the growing population diversity, however, due to unobserved/undefined individual heterogeneity in multiple highly correlated measurements of various disease preventive behaviors, statistical modeling to assess these complex data is challenging. Biomarkers that identify high-risk individuals may improve our understanding of heterogeneity in risk behavioral patterns, but there is a lack of validated approach that can properly link biomarkers to multiple behaviors by determining their dynamic relations with cancer risk. This is because it requires a validation process and advanced statistical methodology that can address various challenges in analyzing biomarker data, including left-censoring due to detection limits. We propose a new statistical approach to disease-preventive behaviors profiling to address these statistical challenges while providing greater flexibility to characterize risk-specific lifestyle behavioral patterns. We evaluate performance of the proposed method through simulations and real data applications.

Biomarkers

Quantile Regression

Lifestyle behaviors

Left-censoring

Longitudinal data 

Caucus for Women in Statistics