Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons

• Published in: Biometrics
• Main Authors: Morrissey, Mary J., Spiegelman, Donna
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 1999
• Subjects: Applied Mathematics; General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; General Medicine; Statistics and Probability; Greenland
• Online Access: http://dx.doi.org/10.1111/j.0006-341x.1999.00338.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.0006-341X.1999.00338.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0006-341X.1999.00338.x

Summary. Misclassification of exposure variables is a common problem in epidemiologic studies. This paper compares the matrix method (Barren, 1977, Biometrics 33 , 414–418; Greenland, 1988a, Statistics in Medicine 7 , 745–757) and the inverse matrix method (Marshall, 1990, Journal of Clinical Epidemiology 43 , 941–947) to the maximum likelihood estimator (MLE) that corrects the odds ratio for bias due to a misclassified binary covariate. Under the assumption of differential misclassification, the inverse matrix method is always more efficient than the matrix method; however, the efficiency depends strongly on the values of the sensitivity, specificity, baseline probability of exposure, the odds ratio, case‐control ratio, and validation sampling fraction. In a study on sudden infant death syndrome (SIDS), an estimate of the asymptotic relative efficiency ( ) of the inverse matrix estimate wasO99, while the matrix method's wasO19. Under nondifferential misclassification, neither the matrix nor the inverse matrix estimator is uniformly more efficient than the other; the efficiencies again depend on the underlying parameters. In the SIDS data, the MLE was more efficient than the matrix method ( ). In a study investigating the effect of vitamin A intake on the incidence of breast cancer, the MLE was more efficient than the matrix method ( ).