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.

中文翻译：

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当部分观察到真实事件数据时，用于估计竞争风险设置中的错误分类概率的伪似然方法

结果错误分类在二元结果研究中经常发生，并可能导致对发生率、流行率、特定原因危害、累积发生率函数等数量的估计有偏差。已经提出了许多补救措施来解决此类数据中结果的潜在错误分类问题。这些补救措施中的大多数在于错误分类概率的估计，这反过来又用于调整结果错误分类的分析。许多作者主张对研究内部的样本使用黄金标准程序来了解错误分类的程度。通过这种类型的内部验证，量化错误分类的问题也变成了缺失数据问题，因为按照设计，真正的结果只能在整个研究样本的一个子集上确定。尽管估计误分类概率的过程在概念上看起来很简单，但迄今为止提出的估计方法在方法论和实践上都存在一些缺陷。大多数方法依赖于缺失的结果数据完全随机缺失 (MCAR)，这是一个相当严格的假设，在实践中不太可能成立。一些现有的方法也往往是计算密集型的。为了解决这些问题，我们在具有可用内部验证样本的研究中，在随机缺失 (MAR) 假设下提出了一种计算效率高、易于实施、错误分类概率的伪似然估计器。我们通过具有竞争风险结果的研究的视角来呈现估计量，尽管估算器超出了此设置。我们描述了所得估计量的一致性和渐近分布特性，并推导出其方差的闭式估计量。该估计器的有限样本性能通过模拟进行评估。使用来自具有竞争风险结果的真实世界研究的数据，我们说明了如何使用所提出的方法来估计错误分类概率。我们还展示了如何在外部研究中使用估计的误分类概率，以在对累积关联函数建模时调整可能的误分类偏差。该估计器的有限样本性能通过模拟进行评估。使用来自具有竞争风险结果的真实世界研究的数据，我们说明了如何使用所提出的方法来估计错误分类概率。我们还展示了如何在外部研究中使用估计的误分类概率，以在对累积关联函数建模时调整可能的误分类偏差。该估计器的有限样本性能通过模拟进行评估。使用来自具有竞争风险结果的真实世界研究的数据，我们说明了如何使用所提出的方法来估计错误分类概率。我们还展示了如何在外部研究中使用估计的误分类概率，以在对累积关联函数建模时调整可能的误分类偏差。