Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction
Küçük Resim Yok
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor and Francis Online
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.
Açıklama
Anahtar Kelimeler
Verification bias, Begg and Greenes correction, Multiple imputation, MAR, NMAR
Kaynak
Journal of Applied Statistics
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
41
Sayı
5
Künye
Ünal, İ. ve Burgut, H. R. (2014). Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction. Journal of Applied Statistics. 41(5), s. 1091-1104.
