The use of linear instrumental variables methods in health services research and health economics: a cautionary note.
OBJECTIVE
To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings.


METHODS
Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data.


METHODS
Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor.


RESULTS
The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods.


CONCLUSIONS
We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity.
Terza
Joseph V
JV
Department of Epidemiology and Health Policy Research, University of Florida, 1329 SW 16th Street, Room 5130, PO Box 100147, Gainesville, FL 32610-0147, USA.
Bradford
W David
WD
Dismuke
Clara E
CE
D015982:Bias; D004470:Economics, Medical / Q000706:statistics & numerical data; D006302:Health Services Research / Q000191:economics / Q000706:statistics & numerical data*; D016014:Linear Models; D017059:Models, Econometric; D014481:United States