In generalized regression models the effect of continuous covariates is commonly assumed to be linear. This assumption, however, may be too restrictive in applications and may lead to biased effect estimates and decreased predictive ability. While a multitude of alternatives for the flexible modeling of continuous covariates have been proposed, methods that provide guidance for choosing a suitable functional form are still limited. To address this issue, we propose a detection algorithm that evaluates several approaches for modeling continuous covariates and guides practitioners to choose the most appropriate alternative. The algorithm utilizes a unified framework for tree-structured modeling which makes the results easily interpretable. We assessed the performance of the algorithm by conducting a simulation study. To illustrate the proposed algorithm, we analyzed data of patients suffering from chronic kidney disease.

翻译：在广义回归模型中，连续协变量的效应通常被假定为线性。然而，这一假设在应用中可能过于严格，导致效应估计产生偏倚并降低预测能力。尽管已有多种替代方法用于连续协变量的灵活建模，但为选择合适函数形式提供指导的方法仍然有限。为解决这一问题，我们提出一种检测算法，该算法评估多种连续协变量建模方法，并指导实践者选择最合适的替代方案。该算法利用统一的树结构建模框架，使结果易于解释。通过仿真研究评估了算法的性能。为说明所提算法，我们分析了慢性肾脏病患者的数据。