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Reliable Diagnosis and Prognosis of COVID-19
Published in Varun Bajaj, G.R. Sinha, Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021
Marjan Mansourian, Hamid Reza Marateb, Maja von Cube, Sadaf Khademi, Mislav Jordanic, Miguel Ángel Mañanas, Harald Binder, Martin Wolkewitz
Some form of internal validation is required for new prediction models to quantify any optimism in predictive performance, such as discrimination and calibration [32]. Bootstrapping or cross-validation are necessary parts of model development [51]. After performing internal validation, to make sure that overfitting does not occur, it is highly recommended to test the performance of the developed model on a different dataset, which is known as external validation [52,53]. Some primary considerations must be taken into account to guard against testing hypotheses suggested by the data (Type III errors [54]). Hold-out validation is not sufficient, and cross-validation, .632+ bootstrapping, or repeated hold-out must also be used [55]. It was shown in the literature that the hold-out validation could lead to significant overestimation of the performance of the diagnosis system, compared with the cross-validation [48].
Selecting an Experimental Design
Published in Perry D. Haaland, Experimental Design in Biotechnology, 2020
A Type in error is more difficult to quantify than the first two types. It is defined as "answering the wrong question". For example, a Type III error arises if the most important variable affecting process performance isn’t included in the experimental design. Another Type in error occurs if, for example, process yield is greatly improved but the resulting loss of purity prevents successful extraction of the target ingredients. Unfortunately, a Type III error cannot be prevented by increasing the sample size or choosing a better design. The best prevention for Type III errors is good communication and clear thinking.
On scoping a test that addresses the wrong objective
Published in Quality Engineering, 2019
Thomas H. Johnson, Rebecca M. Medlin, Laura J. Freeman, James R. Simpson
An adequate experiment requires sufficient power, but more importantly it requires the hypothesis tests to accurately reflect the test objectives. If an experiment provides adequate power, but addresses the wrong objective, we might say an error is committed. Kimball (1957) refers to this error as an error of the third kind, stating, “[A Type III error] is the error committed by giving the right answer to the wrong question.”