Bayesian confidence intervals of proportion with misclassified binary data

2013 ◽  
Vol 42 (3) ◽  
pp. 291-299 ◽  
Author(s):  
Seung-Chun Lee
2020 ◽  
Vol 29 (10) ◽  
pp. 3006-3018 ◽  
Author(s):  
Guogen Shan

Clustered binary data are commonly encountered in many medical research studies with several binary outcomes from each cluster. Asymptotic methods are traditionally used for confidence interval calculations. However, these intervals often have unsatisfactory performance with regards to coverage for a study with a small sample size or the actual proportion near the boundary. To improve the coverage probability, exact Buehler’s one-sided intervals may be utilized, but they are computationally intensive in this setting. Therefore, we propose using importance sampling to calculate confidence intervals that almost always guarantee the coverage. We conduct extensive simulation studies to compare the performance of the existing asymptotic intervals and the new accurate intervals using importance sampling. The new intervals based on the asymptotic Wilson score for sample space ordering perform better than others, and they are recommended for use in practice.


2008 ◽  
Vol 47 (06) ◽  
pp. 470-479 ◽  
Author(s):  
W. Vach ◽  
P.F. Høilund-Carlsen ◽  
O. Gerke

Summary Objectives: When the combined diagnostic imaging technique PET/CT is considered promising with respect to diagnosis/staging of a certain cancer type, a systematic investigation by means of clinical diagnostic studies in the target population is necessary to evaluate the usefulness of PET/CT compared to the current standard. It is often difficult to decide in advance whether it is appropriate to plan a superiority or non-inferiority study. We propose a statistical analysis strategy which is flexible enough to cope with both aims alike. Methods: In opposition to clinical studies on drugs, each patient can be subjected to both PET/CT and the current standard, leading to paired observations of binary data (e.g., cancer = yes/no, stage = 0/1+). The analysis strategy focuses on point estimates and confidence intervals for the difference (or relative increase) in accuracy measures. Results: Formulas for approximate 95% confidence intervals for the differences in sensitivity, specificity, positive and negative predictive values between PET/CT and the standard procedures are given, respectively. The strategy can also be applied if results obtained with a golden standard are not available in patients in whom both PET/CT and the standard procedure gave negative results. Sample sizes can and should be determined in an adaptive manner. Conclusions: Diagnostic studies to assess the merit of PET/CT in the diagnostic work-up of cancer patients can and should start with phase II studies focusing on 95% confidence intervals for differences in diagnostic measures. Even if the gold standard procedure is incomplete, the statistical analysis strategy given here may still be applicable.


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