CAUSALITY, MEASUREMENT ERROR AND MULTICOLLINEARITY IN EPIDEMIOLOGY
1996; Wiley; Volume: 7; Issue: 4 Linguagem: Inglês
10.1002/(sici)1099-095x(199607)7
ISSN1180-4009
AutoresJames V. Zidek, Hubert Wong, Nhu D. Le, Rick Burnett,
Tópico(s)Statistical Methods and Inference
ResumoThis 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 non-linear 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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