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A New Ratio for Protocol Categorization
Author(s) -
Pierre Squara
Publication year - 2014
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2014/389845
Subject(s) - predictability , categorization , contingency table , statistics , matching (statistics) , correlation , correlation coefficient , computer science , mathematics , econometrics , data mining , artificial intelligence , geometry
The present review describes and validates a new ratio “ S ” created for matching predictability and balance between TP and TN. Validity of S was studied in a three-step process as follows: (i) S was applied to the data of a past study predicting cardiac output response to fluid bolus from response to passive leg raise (PLR); (ii) S was comparatively analyzed with traditional ratios by modeling different 2 ∗ 2 contingency tables in 1000 hypothetical patients; (iii) precision of S was compared with other ratios by computing random fluctuations in the same patients. In comparison to other ratios, S performs better in predicting the cardiac response to fluid bolus and supports more directly the clinical conclusions. When the proportion of false responses is high, S is close to the coefficient correlation (CC). When the proportion of true responses is high, S is the unique ratio that identifies the categorization that balances the proportion of TP and TN. The precision of S is close to that of CC. In conclusion, S should be considered for creating categories from quantitative variables; especially when matching predictability with balance between TP and TN is a concern.

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