Outcome phenotype measurement error is rarely corrected in comparative effect estimation studies in observational pharmacoepidemiology. Quantitative bias analysis (QBA) is a misclassification correction method that algebraically adjusts person counts in exposure-outcome contingency tables to reflect the magnitude of misclassification. The extent QBA minimizes bias is unclear because few systematic evaluations have been reported. We empirically evaluated QBA impact on odds ratios (OR) in several comparative effect estimation scenarios. We estimated non-differential and differential phenotype errors with internal validation studies using a probabilistic reference. Further, we synthesized an analytic space defined by outcome incidence, uncorrected ORs, and phenotype errors to identify which combinations produce invalid results indicative of input errors. We evaluated impact with relative bias [(OR-ORQBA)]/OR*100%]. Results were considered invalid if any contingency table cell was corrected to a negative number. Empirical bias correction was greatest in lower incidence scenarios where uncorrected ORs were larger. Similarly, synthetic bias correction was greater in lower incidence settings with larger uncorrected estimates. The invalid proportion of synthetic scenarios increased as uncorrected estimates increased. Results were invalid in common, low incidence scenarios indicating problematic inputs. This demonstrates the importance of accurately and precisely estimating phenotype errors before implementing QBA in comparative effect estimation studies.

翻译：结局表型测量误差在观察性药物流行病学的比较效应估计研究中很少得到校正。定量偏倚分析是一种误分类校正方法，通过代数方法调整暴露-结局列联表中的人数，以反映误分类的程度。由于缺乏系统性评估，定量偏倚分析最小化偏倚的程度尚不明确。我们在多个比较效应估计场景中实证评估了定量偏倚分析对优势比的影响。通过使用概率性参考的内部验证研究，我们估计了非差异性和差异性表型误差。此外，我们合成了一个由结局发生率、未校正优势比和表型误差定义的分析空间，以识别哪些组合会产生指示输入误差的无效结果。我们通过相对偏倚[(OR-ORQBA)]/OR*100%]评估影响。若任何列联表单元格被校正为负数，则结果被视为无效。在未校正优势比较大的低发生率场景中，实证偏倚校正效果最为显著。类似地，在未校正估计值较大的低发生率设置中，合成偏倚校正效果也更大。随着未校正估计值的增加，合成场景的无效比例上升。在常见但低发生率的场景中，结果呈现无效，表明存在问题输入。这证明了在比较效应估计研究中实施定量偏倚分析之前，准确且精确地估计表型误差的重要性。