Incorporating survey weights in tail models for population blood pressure distribution

Music City Center 

Understanding changes over time in population blood pressure (BP) from a nationally-representative survey (e.g., NHANES) requires accurate modeling of both the center and the tail of the BP distribution; a shift in the entire distribution may indicate socioeconomic or cultural trends affecting the whole population, while changes in the high-BP tail may represent changing access to clinical care. The complex survey design used in the NHANES study introduces challenges for modeling the bulk and tail distributions of systolic BP. We propose use of the peaks-over-threshold approach, widely used in climate science and recently adopted in health contexts, where the distribution tail is modeled by a Generalized Pareto Distribution (GPD). We employ pseudo maximum likelihood estimation (PMLE) to accommodate the survey weights. Analytically we determine conditions under which neglecting survey weights may or may not lead to bias in GPD parameter estimates. In particular, estimates of the shape parameter may still be unbiased if tail observations share similar weights. We demonstrate the PMLE approach through simulations and application to BP data in NHANES.

extreme value analysis

peaks over threshold

maximum likelihood estimation

survey weights

blood pressure 

Biometrics Section