Abstract:
Outcome misclassification occurs frequently in binary‐outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause‐specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold‐standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally‐intensive. To address these issues, we propose a computationally‐efficient, easy‐to‐implement, pseudo‐likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal‐validation sample. We present the estimator through the lens of studies with competing‐risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed‐form estimator of its variance. The finite‐sample performance of this estimator is evaluated via simulations. Using data from a real‐world study with competing‐risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions.