Modeling nonlinear dose-response relationships in epidemiologic studies: statistical approaches and practical challenges.
Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationships. The examples presented reveal that no single approach is universally appropriate; rather, these (and possibly other) nonlinearities require for their discovery a variety of both graphical and numeric techniques.
May
Susanne
S
Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0717, USA. smay@ucsd.edu
Bigelow
Carol
C