The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models
Maria Paola Caria
Karolinska Institutet, Sweden
Avogadro University, Novara, Italy
[email protected]
|
Maria Rosaria Galanti
Karolinska Institutet, Sweden
[email protected]
|
Rino Bellocco
Karolinska Institutet, Sweden
University of Milano–Bicocca, Milan, Italy
[email protected]
|
Nicholas J. Horton
Smith College, Northampton, MA
[email protected]
|
Abstract. Multiple-source data are often collected to provide better information
of some underlying construct that is difficult to measure or likely to be missing.
In this article, we describe regression-based methods for analyzing multiple-source
data in Stata. We use data from the BROMS Cohort Study, a cohort of Swedish
adolescents who collected data on body mass index that was self-reported and
that was measured by nurses. We draw together into a single frame of reference
both source reports and relate these to smoking onset. This unified method has
two advantages over traditional approaches: 1) the relative predictiveness of each
source can be assessed and 2) all subjects contribute to the analysis. The methods
are applicable to other areas of epidemiology where multiple-source reports are
used.
View all articles by these authors:
Maria Paola Caria, Maria Rosaria Galanti, Rino Bellocco, Nicholas J. Horton
View all articles with these keywords:
multiple informants, multiple-source predictors, regression analysis, generalized estimating equations, missing data
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
