On the nondifferential misclassification of a binary confounder

Elizabeth L. Ogburn, Tyler J. VanderWeele

Research output: Contribution to journalArticlepeer-review

Abstract

Consider a study with binary exposure, outcome, and confounder, where the confounder is nondifferentially misclassified. Epidemiologists have long accepted the unproven but oft-cited result that, if the confounder is binary, then odds ratios, risk ratios, and risk differences that control for the mismeasured confounder will lie between the crude and the true measures. In this paper, we provide an analytic proof of the result in the absence of a qualitative interaction between treatment and confounder, and we demonstrate via counterexample that the result need not hold when there is such a qualitative interaction. We also present an analytic proof of the result for the effect of treatment among the treated and describe extensions to measures conditional on or standardized over other covariates.

Original languageEnglish (US)
Pages (from-to)433-439
Number of pages7
JournalEpidemiology
Volume23
Issue number3
DOIs
StatePublished - May 2012
Externally publishedYes

ASJC Scopus subject areas

  • Epidemiology

Fingerprint

Dive into the research topics of 'On the nondifferential misclassification of a binary confounder'. Together they form a unique fingerprint.

Cite this