title: A new method to address verification bias in studies of clinical screening tests: cervical cancer screening assays as an example.
abstract: OBJECTIVE
  Studies to evaluate clinical screening tests often face the problem that the "gold standard" diagnostic approach is costly and/or invasive. It is therefore common to verify only a subset of negative screening tests using the gold standard method. However, undersampling the screen negatives can lead to substantial overestimation of the sensitivity and underestimation of the specificity of the diagnostic test. Our objective was to develop a simple and accurate statistical method to address this "verification bias."
  
  
  METHODS
  We developed a weighted generalized estimating equation approach to estimate, in a single model, the accuracy (eg, sensitivity/specificity) of multiple assays and simultaneously compare results between assays while addressing verification bias. This approach can be implemented using standard statistical software. Simulations were conducted to assess the proposed method. An example is provided using a cervical cancer screening trial that compared the accuracy of human papillomavirus and Pap tests, with histologic data as the gold standard.
  
  
  RESULTS
  The proposed approach performed well in estimating and comparing the accuracy of multiple assays in the presence of verification bias.
  
  
  CONCLUSIONS
  The proposed approach is an easy to apply and accurate method for addressing verification bias in studies of multiple screening methods.
authors: Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Belfer 1303C, Bronx, NY 10461, USA. Electronic address: Xiaonan.xue@einstein.yu.edu.
authors: Xiaonan
authors: X
authors: Xue
authors: Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Belfer 1303C, Bronx, NY 10461, USA.
authors: Mimi Y
authors: MY
authors: Kim
authors: Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Belfer 1303C, Bronx, NY 10461, USA.
authors: Philip E
authors: PE
authors: Castle
authors: Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Belfer 1303C, Bronx, NY 10461, USA.
authors: Howard D
authors: HD
authors: Strickler
mesh_terms: D015982:Bias; D001699:Biometry; D055088:Early Detection of Cancer / Q000379:methods* / Q000592:standards*; D005260:Female; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis*
keywords: Clinical screening tests; Positive and negative predictive values; Sensitivity; Specificity; Verification bias; Weighted generalized estimating equations