Folgado-de la Rosa David Manuel, Palazón-Bru Antonio, Gil-Guillén Vicente Francisco
Department of Information Technology (IT), CrossLend GmbH, Berlin, Germany.
Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
Comput Methods Programs Biomed. 2020 Nov;196:105570. doi: 10.1016/j.cmpb.2020.105570. Epub 2020 Jun 3.
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.
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.
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.
Our methodology may be applied to other point systems for predicting binary events, as well as to other types of predictive models.
要使用基于逻辑回归模型的评分系统来预测特定人群中的二元事件，对该系统进行验证很有必要。最正确的做法是通过自助法计算区分度和校准度。区分度可通过受试者工作特征曲线下面积（AUC）来衡量，校准度则通过平滑校准图的呈现来衡量（最推荐的方法）。由于这并非一项简单任务，我们开发了一种方法来构建一款安卓移动应用程序以执行此任务。
该应用程序的构建基于安卓支持的语言编写的源代码。它旨在使用待分析对象的数据库，并能够应用科学文献中广泛使用的统计方法来验证评分系统（自助法、AUC、逻辑回归模型和平滑曲线）。例如，我们的方法应用于模拟评分系统数据（doi: 10.1111/ijcp.12851）以预测重症监护病房入院时的死亡率（谷歌应用商店：ICU死亡率）。将结果与在R统计软件包中应用相同方法获得的结果进行比较。
移动应用程序获得的结果与R统计软件包的结果之间未发现差异，应用相同数学技术时这是预期结果。
我们的方法可应用于其他预测二元事件的评分系统，以及其他类型的预测模型。
