alexa Effect of nondifferential misclassification on estimates of odds ratios with multiple levels of exposure.
Mathematics

Mathematics

Journal of Biometrics & Biostatistics

Author(s): Birkett NJ

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Abstract Nondifferential misclassification of exposure status with a dichotomous exposure will produce biased estimates of odds ratios such that the misclassified odds ratio is always biased toward the null value. However, when an exposure classification has more than two levels, empirical data indicate that the direction of bias is less predictable. Analysis of an algebraic model of multi-level exposure misclassification reveals that all odds ratios based on the misclassified data are constrained between the nonmisclassified odds ratio for the most extreme category and the inverse of this value. This implies that the misclassified odds ratio for the most extreme exposure level will be biased toward the null but that odds ratios for intermediate levels of exposure could be biased away from the null value. Further, the amount of bias depends not only on the misclassification rates but also on the distribution of subjects across exposure levels. If it is assumed that misclassification is likely to occur only between adjacent categories, the range of possible misclassified odds ratios is reduced but is still sufficient to permit serious distortion of an exposure-response relation. In general, biases away from the null occur only for intermediate levels of exposure. Reversal of an exposure-response relation is more likely to occur when misclassification rates are high (especially between nonadjacent levels) and when the number of exposure levels is low.
This article was published in Am J Epidemiol and referenced in Journal of Biometrics & Biostatistics

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