• Likelihood;
  • Logistic regression;
  • Measurement error;
  • Regression calibration;
  • Retrospective study;
  • Skewnormal distribution

Summary We investigate the use of prospective likelihood methods to analyze retrospective case–control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case–control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case–control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case–control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer.