๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

CAUSALITY, MEASUREMENT ERROR AND MULTICOLLINEARITY IN EPIDEMIOLOGY

โœ Scribed by JAMES V. ZIDEK; HUBERT WONG; NHU D. LE; RICK BURNETT

Publisher
John Wiley and Sons
Year
1996
Tongue
English
Weight
717 KB
Volume
7
Category
Article
ISSN
1180-4009

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โœฆ Synopsis

This paper demonstrates that measurement error can conspire with multicollinearity among explanatory variables to mislead an investigator. A causal variable measured with error may be overlooked and its significance transferred to a surrogate. The latter's significance can then be entirely spurious, in that controlling it will not predictably change the response variable. In epidemiological research, such a response may be a health outcome. The phenomenon we study is demonstrated through simulation experiments involving nonlinear regression models. Also, the paper presents the measurement error problem in a theoretical setting. The paper concludes by echoing the familiar warning against making conclusions about causality from a multiple regression analysis, in this case because of the phenomenon presented in the paper.

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