The use of discretized variables in the development of prediction models is a common practice, in part because the decision-making process is more natural when it is based on rules created from segmented models. Although this practice is perhaps more common in medicine, it is extensible to any area of knowledge where a predictive model helps in decision-making. Therefore, providing researchers with a useful and valid categorization method could be a relevant issue when developing prediction models. In this paper, we propose a new general methodology that can be applied to categorize a predictor variable in any regression model where the response variable belongs to the exponential family distribution. Furthermore, it can be applied in any multivariate context, allowing to categorize more than one continuous covariate simultaneously. In addition, a computationally very efficient method is proposed to obtain the optimal number of categories, based on a pseudo-BIC proposal. Several simulation studies have been conducted in which the efficiency of the method with respect to both the location and the number of estimated cut-off points is shown. Finally, the categorization proposal has been applied to a real data set of 543 patients with chronic obstructive pulmonary disease from Galdakao Hospital's five outpatient respiratory clinics, who were followed up for 10 years. We applied the proposed methodology to jointly categorize the continuous variables six-minute walking test and forced expiratory volume in one second in a multiple Poisson generalized additive model for the response variable rate of the number of hospital admissions by years of follow-up. The location and number of cut-off points obtained were clinically validated as being in line with the categorizations used in the literature.

翻译：在预测模型开发中使用离散化变量是一种常见做法，部分原因是基于分段模型构建的规则更便于决策过程。虽然这种做法在医学领域可能更为普遍，但它可扩展到任何依赖预测模型辅助决策的知识领域。因此，为研究者提供实用且有效的分类方法，对预测模型开发具有重要意义。本文提出一种新的通用方法，可对响应变量服从指数族分布的任意回归模型中的预测变量进行分类，同时适用于多变量场景，支持同时分类多个连续协变量。此外，我们提出一种基于伪BIC准则的高效计算方法，用于确定最优分类数量。通过多项模拟研究，验证了该方法在估计切分点位置与数量方面的有效性。最后，将该分类方法应用于一项包含543例慢性阻塞性肺疾病患者真实数据集（来自加尔达考医院五家呼吸专科门诊，随访10年）。我们采用所提方法，在泊松广义可加模型中，以年住院次数率为响应变量，对连续变量六分钟步行试验和第一秒用力呼气量进行联合分类。经临床验证，所得切分点位置与数量与文献中使用的分类标准一致。