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One would therefore choose a specificity close to one and could read from the ROC curve the achievable sensitivity based on the underlying classification model.
Thus, users can choose a specificity level higher than the default value (85%) to reduce the number of false positive predictions.
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We first randomly chose an organ specificity index, uniformly from the interval of (τmin, τmax), where τmin and τmax are the lowest and the highest values of the observed τ, respectively.
In this scenario, the OR of choosing a test with 95% specificity was 1.24 times that of a test with 50% specificity (WTP = $827).
Therefore, under scenario B, the odds of choosing a test with 95% specificity were 1.50 times the odds of choosing a test with 50% specificity and the corresponding WTP was $1080.
Preference weights and WTPs for a test with sensitivity of 80% or 65% in scenario B were not significantly different from corresponding values in scenario A. In scenario A, the odds of choosing a test with 95% specificity were 1.24 times the odds of choosing a test with 50% specificity and the public was willing to pay $827 for this amount of improvement in specificity level.
Cutoff titers for IgM and IgG were chosen to achieve a specificity >98%; sensitivity varied between different rickettsial antigens.
With a CA125 threshold of 125 U/ml, chosen to obtain a specificity of 98%, the CA125 sensitivity was approximately 71% (45/63).
A threshold of 13.7 kPa was chosen with specificity and a positive predictive value at 100% (sensitivity, 93%; negative predictive value, 94%).
In non-aggressive incurable cancer (scenario B), the OR of choosing a test with 95% sensitivity was 1.65 (WTP = $1344), and the OR of choosing a test with 95% specificity was 1.50 (WTP = $1080).
We chose an indirect way to evaluate specificity and significance of regions reported by ADMIRE but not by RnBeads.
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