[Implication of inverse-probability weighting method in the evaluation of diagnostic test with verification bias].
To evaluate and adjust the verification bias existed in the screening or diagnostic tests. Inverse-probability weighting method was used to adjust the sensitivity and specificity of the diagnostic tests, with an example of cervical cancer screening used to introduce the Compare Tests package in R software which could be implemented. Sensitivity and specificity calculated from the traditional method and maximum likelihood estimation method were compared to the results from Inverse-probability weighting method in the random-sampled example. The true sensitivity and specificity of the HPV self-sampling test were 83.53% (95%CI:74.23-89.93)and 85.86% (95%CI: 84.23-87.36). In the analysis of data with randomly missing verification by gold standard, the sensitivity and specificity calculated by traditional method were 90.48% (95%CI:80.74-95.56)and 71.96% (95%CI:68.71-75.00), respectively. The adjusted sensitivity and specificity under the use of Inverse-probability weighting method were 82.25% (95% CI:63.11-92.62) and 85.80% (95% CI: 85.09-86.47), respectively, whereas they were 80.13% (95%CI:66.81-93.46)and 85.80% (95%CI: 84.20-87.41) under the maximum likelihood estimation method. The inverse-probability weighting method could effectively adjust the sensitivity and specificity of a diagnostic test when verification bias existed, especially when complex sampling appeared.
Department of Cancer Epidemiology, Cancer Institute of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Leni
L
Kang
Department of Cancer Epidemiology, Cancer Institute of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Shaokai
S
Zhang
Department of Cancer Epidemiology, Cancer Institute of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Fanghui
F
Zhao
Department of Cancer Epidemiology, Cancer Institute of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China. Email: qiaoy@cicams.ac.cn.
Youlin
Y
Qiao
D019937:Diagnostic Techniques and Procedures; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D012680:Sensitivity and Specificity; D012984:Software; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis