Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27790
Title: Is a cutoff of 10% appropriate for the change-in-estimate criterion of confounder identification?
Authors: Lee, PH
Keywords: Causality
Confounding factors
Regression
Simulation
Statistical models
Issue Date: 2014
Publisher: Japan Epidemiological Assoc
Source: Journal of epidemiology, 2014, v. 24, no. 2, p. 161-167 How to cite?
Journal: Journal of Epidemiology 
Abstract: Background: When using the change-in-estimate criterion, a cutoff of 10% is commonly used to identify confounders. However, the appropriateness of this cutoff has never been evaluated. This study investigated cutoffs required under different conditions. Methods: Four simulations were performed to select cutoffs that achieved a significance level of 5% and a power of 80%, using linear regression and logistic regression. A total of 10 000 simulations were run to obtain the percentage differences of the 4 fitted regression coefficients (with and without adjustment). Results: In linear regression, larger effect size, larger sample size, and lower standard deviation of the error term led to a lower cutoff point at a 5% significance level. In contrast, larger effect size and a lower exposure-confounder correlation led to a lower cutoff point at 80% power. In logistic regression, a lower odds ratio and larger sample size led to a lower cutoff point at a 5% significance level, while a lower odds ratio, larger sample size, and lower exposure-confounder correlation yielded a lower cutoff point at 80% power. Conclusions: Cutoff points for the change-in-estimate criterion varied according to the effect size of the exposure-outcome relationship, sample size, standard deviation of the regression error, and exposure-confounder correlation.
URI: http://hdl.handle.net/10397/27790
ISSN: 0917-5040
DOI: 10.2188/jea.JE20130062
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