A method to validate scoring systems based on logistic regression models to predict binary outcomes via a mobile application for Android with an example of a real case. (November 2020)

• Record Type: Journal Article
• Authors: Folgado-de la Rosa, David Manuel; Palazón-Bru, Antonio; Gil-Guillén, Vicente Francisco
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Mobile applications -- Models -- Statistical software -- Validation -- Validation studies as topic; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105570
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This article describes a methodology to construct a mobile application for Android. This externally validates prediction models using current statistical recommendations. We explain how to develop the application in question. The methodology is applied to a real points system (ICU Predictor in Google Play). Our methodology could be applied to any points system and similar predictive models. Abstract: Background and objectives: To use a points system based on a logistic regression model to predict a binary event in a given population, the validation of this system is necessary. The most correct way to do this is to calculate discrimination and calibration using bootstrapping. Discrimination can be addressed through the area under the receiver operating characteristic curve (AUC) and calibration through the representation of the smoothed calibration plot (most recommended method). As this is not a simple task, we developed a methodology to construct a mobile application in Android to perform this task. Methods: The construction of the application is based on source code written in language supported by Android. It is designed to use a database of subjects to be analyzed and to be able to apply statistical methods widely used in the scientific literature to validate a points system (bootstrap, AUC, logistic regression models and smooth curves). As an example our methodology was applied on simulated points system data (doi: 10.1111/ijcp.12851) to predict mortality on admission to intensive care units (Google Play: ICU mortality ). The results were compared with those obtained applying the same methods in the R statistical package. Results: No differences were found between the results obtained in the mobile application and those from the R statistical package, an expected result when applying the same mathematical techniques. Conclusions: Our methodology may be applied to other point systems for predicting binary events, as well as to other types of predictive models.