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6. Repeat steps 1 - 6 for a range of threshold probabilities.
3. Calculate the ERg SUPPORT using equation 5. 4. Calculate ERg(Rx) using equation 4. 5. Compute the ERg(Hosp using equation 3. 6. Repeat steps 1 - 6 for a range of threshold probabilities.
Briefly, decision curve analysis estimates the net benefit of a model by taking the difference between the number of true positives and the number of false positives weighted by the odds of the selected threshold probability of risk for a range of threshold probabilities (25, 40).
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The TNM nomogram showed favorable net benefits with a range of threshold probabilities from 0 to 42%.
We anticipate that for a different range of threshold probabilities these models may perform better than the SUPPORT model.
As can be seen from Figures 5, 6 and 7 the optimal decision is derived by the SUPPORT model for a rather wide range of threshold probabilities.
We might therefore consider the value of the full model confirmed for the entire range of threshold probabilities that a man would typically require for a prostate biopsy.
Thus, a reasonable range of threshold probabilities might indeed be between 8% - 43% as suggested by our model.
However, decisions about the value of the model are not likely to be affected because the model remains of value over a wide range of threshold probabilities.
The CEAC will plot the probability of the intervention being cost-effective for a range of threshold values of an additional QALY.
As we do not know the threshold willingness to pay value for additional effectiveness associated with CBT, the probability that CBT will be considered cost effective was calculated for a range of threshold values and presented in a cost effectiveness acceptability curve (CEAC).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com