Small Area Estimation in Health Sciences


Chair: Partha Lahiri

Small area estimation (SAE) has received considerable attention in recent years due to growing demand for reliable small area statistics that are needed in formulating policies and programs, allocation of government funds, regional planning, marketing decisions at local level, and other uses. Without any doubt, small area estimation is now one of the fastest growing research areas in statistics. In particular, one of the major applications of small area models is in the health sciences set-up where advanced statistical techniques in mixed models have been developed. Some health-related applications of SAE are (but not limited to) poverty counts of school-age children at the area level, mapping of regional mortality (or incidence) rates of disease (e.g. cancer), and so on. In this session, our aim is to show off some of small area techniques developed to address health-related outcomes.

 


Some SAE methods in Health and Medical Studies

In many problems of health and medical studies, the interest is primarily on individuals, or small groups of individuals. For example, such problems arise in personalized health service. Statistically, the quantities of interest can be expressed as mixed effects, and the statistical challenges can be associated to prediction of mixed effects under a mixed-effects model. […]

Model-based Small Area Estimation for Cancer Screening and Smoking Related Measures

National health surveys, such as the National Health Interview Survey (NHIS), the Behavioral Risk Factor Surveillance System (BRFSS), and the Tobacco Use Supplement to the Current Population Survey (TUS-CPS), have been used to collect data on cancer screening and smoking related measures in the U.S. noninstitutionalized population. These surveys are designed to produce reliable estimates […]

Zero-inflated Spatio-temporal Models for Small Areas

In this talk, our aim is to study geographical and temporal variability of disease incidence when spatio-temporal count data have excess zeros. To that end, we consider random effects in zero-inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio-temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B-spline […]

This session was organised by Mahmoud Torabi. Only logged in users can see slides when author's permission was given. Please register to have access.