Arifin Wan Nor, Yusof Umi Kalsom
Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia.
Albukhary International University, Alor Setar, Kedah, Malaysia.
PLoS One. 2025 Sep 26;20(9):e0321440. doi: 10.1371/journal.pone.0321440. eCollection 2025.
Diagnostic accuracy studies are crucial for evaluating new tests before their clinical application. These tests are compared with gold standard tests, and accuracy measures such as sensitivity (Sn) and specificity (Sp) are often calculated. However, these studies frequently suffer from partial verification bias (PVB) due to selective verification of patients, which leads to biased accuracy estimates. Among the methods for correcting PVB under the missing-at-random assumption for binary diagnostic tests, a bootstrap-based method known as the inverse probability bootstrap (IPB) was proposed. IPB demonstrated low bias for estimating Sn and Sp, but exhibited higher standard errors (SE) than other PVB correction methods and only corrected the distribution of the verified portion of the PVB data. This paper introduces two new methods to address these limitations: scaled inverse probability weighted resampling (SIPW) and scaled inverse probability weighted balanced resampling (SIPW-B), both built upon IPB. Using simulated and clinical datasets, SIPW and SIPW-B were compared against IPB and other existing methods (Begg and Greenes', inverse probability weighting estimator, and multiple imputation). For the simulated data sets, different combinations of disease prevalence (0.4 and 0.1), Sn (0.3 to 0.9), Sp (0.6 and 0.9), and sample sizes (200 and 1000) were generated. Two commonly used clinical datasets in PVB correction studies were also used. Performance was evaluated using bias and SE for the simulated data. Simulation results showed that both new methods outperformed IPB by producing lower bias and SE for Sn and Sp estimation, showing results comparable to existing methods, and demonstrating good performance at low disease prevalence. In clinical datasets, SIPW and SIPW-B were consistent with existing methods. The new methods also improve upon IPB by allowing full data restoration. Although the methods are computationally demanding at present, this limitation is expected to become less important as computing power continues to increase.
诊断准确性研究对于在新测试临床应用前进行评估至关重要。这些测试会与金标准测试进行比较，并且常常计算诸如灵敏度（Sn）和特异度（Sp）等准确性指标。然而，由于对患者的选择性验证，这些研究经常受到部分验证偏倚（PVB）的影响，这会导致准确性估计出现偏差。在针对二元诊断测试的随机缺失假设下校正PVB的方法中，提出了一种基于自助法的方法，即逆概率自助法（IPB）。IPB在估计Sn和Sp时显示出低偏差，但与其他PVB校正方法相比，其标准误（SE）更高，并且仅校正了PVB数据已验证部分的分布。本文介绍了两种新方法来解决这些局限性：缩放逆概率加权重采样（SIPW）和缩放逆概率加权平衡重采样（SIPW - B），这两种方法均基于IPB构建。使用模拟数据集和临床数据集，将SIPW和SIPW - B与IPB及其他现有方法（Begg和Greenes方法、逆概率加权估计器以及多重填补法）进行比较。对于模拟数据集，生成了疾病患病率（0.4和0.1）、Sn（0.3至0.9）、Sp（0.6和0.9）以及样本量（200和1000）的不同组合。还使用了PVB校正研究中两个常用的临床数据集。对于模拟数据，使用偏差和SE来评估性能。模拟结果表明，两种新方法在估计Sn和Sp时均表现优于IPB，产生的偏差和SE更低，结果与现有方法相当，并且在低疾病患病率时表现良好。在临床数据集中，SIPW和SIPW - B与现有方法一致。新方法还通过允许完全数据恢复改进了IPB。尽管目前这些方法对计算要求较高，但随着计算能力的不断提高，这一局限性预计将变得不那么重要。
