How to know the power of an AUC test in medical esearch?
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The power of an AUC test in medical research can be assessed by evaluating the discrimination ability of a diagnostic test through the Receiver Operating Characteristic (ROC) curve. The Area Under the ROC Curve (AUC) is a key metric representing the probability that a randomly chosen diseased individual will have a higher probability of disease than a randomly chosen non-diseased individual. Various methods, such as the Binormal model and confidence intervals, can be employed to estimate the AUC and assess the performance of the diagnostic test. Adjusting for selection bias and patient covariates can provide more accurate estimations of the AUC, especially when not all patients undergo disease verification. Analytical Ultracentrifugation (AUC) is a valuable tool in biochemistry and molecular biology, offering insights into macromolecular characteristics that are essential for understanding complex systems.
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Open access•Dissertation 01 Jan 2010 | Not addressed in the paper. |
| Estimate the power of an AUC test in medical research by developing doubly robust estimators that adjust for selection bias and covariates in the presence of verification bias. | |
Open access 01 Jan 2013 | The power of an AUC test in medical research can be assessed by estimating the Area under the Binormal ROC curve using confidence intervals of variances and Kolmogorov‐Smirnov test for normality. |
Open access•Journal Article | The power of an AUC test in medical research can be assessed by evaluating the discrimination ability of a diagnostic test through the area under the ROC curve (AUC). |
01 Dec 2020 10 Citations | Not addressed in the paper. |
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