The Impact of Joint Misclassification of Exposures and Outcomes on the Results of Epidemiologic Research

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

“…which suggests the plug-in estimator (6) where b E denotes the sample mean operator and b W the sample analogue (i.e. consistent estimator) of W in equation (4).…”

“…14 The only difference is that where in equation 7it does not depend on the fallible measurement Z of Y, Gravel and Platt define different weights for subjects with Z ¼ 0. Note, however, that the choice of weights for subjects with Z ¼ 0 does not affect the population quantity in equation (5) or the estimator defined by equation (6), because the weights only appear in products with Z, which equal zero if Z ¼ 0.…”

“…In particular, joint misclassification of exposure and outcome can lead to considerable bias of standard causal effect estimators, with direction and magnitude depending on various factors, including the misclassification mechanism and the direction and magnitude of the true effect. [1][2][3][4][5][6] Exposure and outcome misclassification is typically categorised according to two separate properties: whether or not the misclassification is differential and whether or not it is dependent relative to some covariate vector L containing patient characteristics. 1,5 Joint misclassification of exposure and outcome is said to be nondifferential if (1) the sensitivity and specificity of exposure classification are constant across all categories of the (true) outcome given L and (2) the sensitivity and specificity of outcome classification are constant across all categories of the (true) exposure given L; otherwise it is differential.…”

“…Epidemiological research hampered by joint misclassification of some type is likely voluminous. 6 Examples of studies affected by exposure and outcome misclassification can be found, for example, in the literature on the causal effects of drug use, which is largely based on routinely collected data, where exposures are typically operationalised on the basis of prescription records and where outcomes are often self-reported. [8][9][10][11] In applied epidemiological research, misclassification or some of its potential consequences are often ignored.…”

“…12,13 The assertion often made in the discussion of study results that observed measures of association are biased toward the null under nondifferentiality, for example, is not generally true unless additional conditions are presupposed. 2,6 Methods to adjust for misclassification rely on additional information that can be used to estimate or correct for bias. One potential source of information is validation data obtained through supposedly infallible measurement.…”