de Groot J A H, Janssen K J M, Zwinderman A H, Moons K G M, Reitsma J B
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Stat Med. 2008 Dec 10;27(28):5880-9. doi: 10.1002/sim.3410.
Partial verification refers to the situation where a subset of patients is not verified by the reference (gold) standard and is excluded from the analysis. If partial verification is present, the observed (naive) measures of accuracy such as sensitivity and specificity are most likely to be biased. Recently, Harel and Zhou showed that partial verification can be considered as a missing data problem and that multiple imputation (MI) methods can be used to correct for this bias. They claim that even in simple situations where the verification is random within strata of the index test results, the so-called Begg and Greenes (B&G) correction method underestimates sensitivity and overestimates specificity as compared with the MI method. However, we were able to demonstrate that the B&G method produces similar results as MI, and that the claimed difference has been caused by a computational error. Additional research is needed to better understand which correction methods should be preferred in more complex scenarios of missing reference test outcome in diagnostic research.
部分验证是指一部分患者未通过参考（金）标准进行验证并被排除在分析之外的情况。如果存在部分验证，那么诸如灵敏度和特异性等观察到的（原始）准确性指标很可能存在偏差。最近，哈雷尔和周表明，部分验证可被视为一个缺失数据问题，并且多重填补（MI）方法可用于校正这种偏差。他们声称，即使在索引测试结果分层内验证是随机的简单情况下，与MI方法相比，所谓的贝格和格林斯（B&G）校正方法会低估灵敏度并高估特异性。然而，我们能够证明B&G方法产生的结果与MI方法相似，并且所声称的差异是由计算错误导致的。需要进行更多研究，以更好地理解在诊断研究中参考测试结果缺失的更复杂情况下应优先选择哪种校正方法。
