screen interval
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2020 ◽  
pp. 096914132095078
Author(s):  
Stuart G Baker ◽  
Philip C Prorok

Objective According to the Independent UK Panel on Breast Cancer Screening, the most reliable estimates of overdiagnosis for breast cancer screening come from stop-screen trials Canada 1, Canada 2, and Malmo. The screen-interval overdiagnosis fraction is the fraction of cancers in a screening program that are overdiagnosed. We used the cumulative incidence method to estimate screen-interval overdiagnosis fraction. Our goal was to derive confidence intervals for estimated screen-interval overdiagnosis fraction and adjust for refusers in these trials. Methods We first show that the UK Panel’s use of a 95% binomial confidence interval for estimated screen-interval overdiagnosis fraction was incorrect. We then derive a correct 95% binomial-Poisson confidence interval. We also use the method of latent-class instrumental variables to adjust for refusers. Results For the Canada 1 trial, the estimated screen-interval overdiagnosis fraction was 0.23 with a 95% binomial confidence interval of (0.18, 0.27) and a 95% binomial-Poisson confidence interval of (0.04, 0.41). For the Canada 2 trial, the estimated screen-interval overdiagnosis fraction was 0.16 with a 95% binomial confidence interval of (0.12, 0.19) and a 95% binomial-Poisson confidence interval of (−0.01, 0.32). For the Malmo trial, the estimated screen-interval overdiagnosis fraction was 0.19 with a 95% binomial confidence interval of (0.15, 0.22). Adjusting for refusers, the estimated screen-interval overdiagnosis fraction was 0.26 with a 95% binomial-Poisson confidence interval of (0.03, 0.50). Conclusion The correct 95% binomial-Poisson confidence interval s for the estimated screen-interval overdiagnosis fraction based on the Canada 1, Canada 2, and Malmo stop-screen trials are much wider than the previously reported incorrect 95% binomial confidence intervals. The 95% binomial-Poisson confidence intervals widen as follow-up time increases, an unappreciated downside of longer follow-up in stop-screen trials.



BMC Cancer ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Gautier Defossez ◽  
Alexandre Quillet ◽  
Pierre Ingrand


Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Jize Piao ◽  
Weon Shik Han ◽  
Sungwook Choung ◽  
Kue-Young Kim

For investigating the wellbore flow process in CO2 injection scenarios, coupled wellbore-reservoir (WR) and conventional equivalent porous media (EPM) models were compared with each other. In WR model, during the injection, conditions for the wellbore including pressure and temperature were dynamically changed from the initial pressure (7.45–8.33 MPa) and temperature (52.0–55.9°C) of the storage formation. After 3.35 days, the wellbore flow reached the steady state with adiabatic condition; temperature linearly increased from the well-head (35°C) to the well-bottom (52°C). In contrast, the EPM model neglecting the wellbore process revealed that CO2 temperature was consistently 35°C at the screen interval. Differences in temperature from WR and EPM models resulted in density contrast of CO2 that entered the storage formation (~200 and ~600 kg/m3, resp.). Subsequently, the WR model causing greater density difference between CO2 and brine revealed more vertical CO2 migration and counterflow of brine and also developed the localized salt-precipitation. Finally, a series of sensitivity analyses for the WR model was conducted to assess how the injection conditions influenced interplay between flow system and the localized salt-precipitation in the storage formation.



2016 ◽  
Vol 5 (S6) ◽  
pp. S1070-S1072 ◽  
Author(s):  
Marjolein A. Heuvelmans ◽  
Matthijs Oudkerk


2012 ◽  
Vol 45 (2) ◽  
pp. 32
Author(s):  
MARY ANN MOON
Keyword(s):  


2011 ◽  
Vol 44 (10) ◽  
pp. 52
Author(s):  
KERRI WACHTER
Keyword(s):  


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