Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA.
Cancer Epidemiol. 2012 Apr;36(2):153-60. doi: 10.1016/j.canep.2011.07.001. Epub 2011 Sep 19.
A model is proposed to estimate and compare cervical cancer screening test properties for third world populations when only subjects with a positive screen receive the gold standard test. Two fallible screening tests are compared, VIA and VILI.
We extend the model of Berry et al. [1] to the multi-site case in order to pool information across sites and form better estimates for prevalences of cervical cancer, the true positive rates (TPRs), and false positive rates (FPRs). For 10 centers in five African countries and India involving more than 52,000 women, Bayesian methods were applied when gold standard results for subjects who screened negative on both tests were treated as missing. The Bayesian methods employed suitably correct for the missing screen negative subjects. The study included gold standard verification for all cases, making it possible to validate model-based estimation of accuracy using only outcomes of women with positive VIA or VILI result (ignoring verification of double negative screening test results) with the observed full data outcomes.
Across the sites, estimates for the sensitivity of VIA ranged from 0.792 to 0.917 while for VILI sensitivities ranged from 0.929 to 0.977. False positive estimates ranged from 0.056 to 0.256 for VIA and 0.085 to 0.269 for VILI. The pooled estimates for the TPR of VIA and VILI are 0.871 and 0.968, respectively, compared to the full data values of 0.816 and 0.918. Similarly, the pooled estimates for the FPR of VIA and VILI are 0.134 and 0.146, respectively, compared to the full data values of 0.144 and 0.146. Globally, we found VILI had a statistically significant higher sensitivity but no statistical difference for the false positive rates could be determined.
Hierarchical Bayesian methods provide a straight forward approach to estimate screening test properties, prevalences, and to perform comparisons for screening studies where screen negative subjects do not receive the gold standard test. The hierarchical model with random effects used to analyze the sites simultaneously resulted in improved estimates compared to the single-site analyses with improved TPR estimates and nearly identical FPR estimates to the full data outcomes. Furthermore, higher TPRs but similar FPRs were observed for VILI compared to VIA.
当仅对阳性筛查者进行金标准检测时，提出一种用于估计和比较第三世界人群宫颈癌筛查试验特性的模型。比较两种易出错的筛查试验，VIA 和 VILI。
我们扩展了 Berry 等人的模型[1]，以便在多地点情况下进行信息汇总，并为宫颈癌的流行率、真阳性率（TPR）和假阳性率（FPR）形成更好的估计值。在涉及 5 个非洲国家和印度的 10 个中心，对两种检测均为阴性的受试者的金标准结果被视为缺失，应用贝叶斯方法进行分析。贝叶斯方法适当地纠正了缺失的阴性筛查受试者。该研究对所有病例进行了金标准验证，因此仅使用 VIA 或 VILI 阳性结果（忽略对双重阴性筛查试验结果的验证）的女性的观察结果就可以对基于模型的准确性进行验证，同时考虑到完整数据的结果。
在各地点中，VIA 的灵敏度估计值范围为 0.792 至 0.917，而 VILI 的灵敏度估计值范围为 0.929 至 0.977。VIA 的假阳性估计值范围为 0.056 至 0.256，VILI 的假阳性估计值范围为 0.085 至 0.269。VIA 和 VILI 的 TPR 合并估计值分别为 0.871 和 0.968，而全数据值分别为 0.816 和 0.918。同样，VIA 和 VILI 的 FPR 合并估计值分别为 0.134 和 0.146，而全数据值分别为 0.144 和 0.146。总体而言，我们发现 VILI 的灵敏度具有统计学上的显著提高，但对于假阳性率，无法确定是否存在统计学差异。
分层贝叶斯方法为估计筛查试验特性、流行率以及对不进行金标准检测的阴性筛查者的筛查研究进行比较提供了一种简单直接的方法。同时对各地点进行分析的随机效应分层模型与单地点分析相比，提高了估计值，并使 TPR 估计值几乎与全数据结果相同，而 FPR 估计值则相近。此外，与 VIA 相比，VILI 的 TPR 更高，但 FPR 相似。
