Is a cutoff of 10% appropriate for the change-in-estimate criterion of confounder identification?

Hong Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

75 Citations (Scopus)

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. Lee.
Original languageEnglish
Pages (from-to)161-167
Number of pages7
JournalJournal of Epidemiology
Volume24
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Causality
  • Confounding factors
  • Regression
  • Simulation
  • Statistical models

ASJC Scopus subject areas

  • Epidemiology

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