Title

On Nonparametric Tests for Partially Correlated Data With Application to Public Health Issues

Document Type

Presentation

Publication Date

3-2009

Abstract

Correlated or matched data is frequently collected under many study designs in applied sciences such as the social, behavioral, economic, biological, medical, epidemiologic, health, public health, and drug developmental sciences. Challenges with respect to availability and cost commonly occur with matching observational or experimental study subjects, thus researchers frequently encounter situations where the observed sample consists of a combination of correlated and uncorrelated data. This paper discusses and proposes testing procedures to handle data when partially correlated data is available. Theoretical as well as numerical investigation will be provided. The proposed testing procedures will be applied to real data. These procedures will be of special importance in meta-analysis where partially correlated data is a concern when combining results of various studies.

Sponsorship/Conference/Institution

Eastern North American Region International Biometric Society Annual Conference (ENAR)

Location

San Antonio, TX