Estimators of odds ratio regression parameters in matched case-control studies with covariate measurement error

Andrew B. Forbes, Thomas J. Santner

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


This article studies estimators of the odds ratio and odds ratio regression parameters in finely matched case-control studies containing a binary exposure of primary interest and subject-specific covariates that are subject to measurement error. A retrospective logistic regression model for the binary exposure variable is used. The effect of measurement errors on the conditional maximum likelihood estimator is determined. Three alternatives are considered: bias-corrected, functional, and “transformation” estimators. The asymptotic and small-sample properties of the three competitors are studied. The results are illustrated using data from a case-control study of diet and colon cancer.

Original languageEnglish
Pages (from-to)1075-1084
Number of pages10
JournalJournal of the American Statistical Association
Issue number431
Publication statusPublished - 1 Jan 1995


  • Bias correction
  • Conditional maximum likelihood
  • Errors in variables
  • Logistic regression
  • Retrospective study

Cite this