Palazón-Bru Antonio, Folgado-de la Rosa David Manuel, Cortés-Castell Ernesto, López-Cascales María Teresa, Gil-Guillén Vicente Francisco
Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
Department of Pharmacology, Pediatrics and Organic Chemistry, Miguel Hernández University, San Juan de Alicante, Alicante, Spain.
PLoS One. 2017 May 1;12(5):e0176726. doi: 10.1371/journal.pone.0176726. eCollection 2017.
A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model.
The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.
The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.
In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.
An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.
有人建议采用一个包含至少100个事件和100个非事件的样本量来验证一个预测模型，无论该模型是否正在被验证，且某些因素会影响预测模型的校准（区分度、参数化和发生率）。基于二元逻辑回归模型的评分系统是一种特定类型的预测模型。
本研究的目的是开发一种算法，以确定用于验证基于二元逻辑回归模型的评分系统的样本量，并将其应用于一个案例研究。
该算法基于自助抽样，其中计算了ROC曲线下面积、通过平滑曲线得到的观察到的事件概率以及一种用于确定校准不足的度量（估计校准指数）。为了向感兴趣的研究人员说明其用法，该算法被应用于一个基于二元逻辑回归模型的评分系统，以确定重症监护病房的死亡率。
在所提供的案例研究中，该算法得到了一个包含69个事件的样本量，这低于文献中建议的值。
提供了一种算法，用于找到合适的样本量来验证基于二元逻辑回归模型的评分系统。这可应用于确定其他类似案例中的样本量。
