Regression Models for Mixed Over-Dispersed Poisson and Continuous Clustered Data: Modeling BMI and Number of Cigarettes Smoked Per Day
Article Sidebar
Published
May 1, 2012
Main Article Content
Folefac Atem
Brigham and Women's Hospital, Boston
Julius S. Ngwa
Boston University
Abidemi Adeniji
University of Pittsburgh
Abstract
Clustered data, multiple observations collected on the same experimental unit, is common in epidemiological studies. Bivariate outcome data is often the result of interest in two correlated response variables. An efficient method is presented for dealing with bivariate outcomes when one outcome is continuous and the other is a count using a simple transformation to handle over-dispersed Poisson data. A multilevel analysis was performed on data from the National Health Interview Survey (NHIS) with body mass index (BMI) and the number of cigarettes smoked per day (NCS) as responses. Results show that these random effects models yield misleading results in cases where the data is not transformed.
Article Details
Issue
Section
Articles