A nonparametric changepoint model for stratifying continuous variables under order restrictions and binary outcome
A nonparametric changepoint model for stratifying continuous variables under order restrictions and binary outcome
复制标题
用于在顺序限制和二元结果下分层连续变量的非参数变点模型
DOI：
10.1191/0962280203sm338ra
复制
发表时间：
2003
影响因子：
2.3
通讯作者：
K. Ulm
中科院分区：
文献类型：
--
作者：
G. Salanti;K. Ulm
使用单调回归建模可以是一个有用的替代参数的方法时，连续预测的最佳分层的兴趣。这里在二进制响应的上下文中描述该方法。在这一框架内，我们打算处理两点。首先，我们提出了一种方法，以提高简约的模型，通过应用减少过程的基础上的一系列Fisher精确检验和自助法选择完全单调和减少的模型。其次，我们讨论的情况下，多个预测：一个迭代算法（池相邻违规者算法的扩展）可以应用时，考虑到一个以上的预测变量。由此产生的模型是一个单调的表面，并可以交替地应用到添加剂单调模型所描述的Morton-Jones和同事时，假设解释变量相互作用。单调表面模型还提供了单调似然比检验的多变量扩展。这里讨论了这个测试，并提出了一种基于排列来评估p值的方法。最后，我们结合联合收割机这两个想法（减少单调回归和单调表面估计），以一个简单，易于解释的模型，这导致在几个恒定的风险组的预测组合。尽管事实上，所提出的方法变得有点麻烦，由于缺乏渐近方法来推断，它是有吸引力的，因为它的简单性和稳定性。应用程序将概述在建模中使用双变量阶跃函数的好处。
Modelling using monotonic regression can be a useful alternative to parametric approaches when optimal stratification for continuous predictors is of interest. This method is described here in the context of binary response. Within this framework we aim to address two points. First, we propose a method to enhance the parsimony of the model, by applying a reducing procedure based on a sequence of Fisher exact tests and a bootstrap method to select between full monotonic and reduced model. Secondly, we discuss the case of multiple predictors: an iterative algorithm (an extension of the Pool Adjacent Violators Algorithm) can be applied when more than one predictor variable is taken into account. The resulting model is a monotonic surface and can be applied alternatively to the additive monotonic models as described by Morton-Jones and colleagues when the explanatory variables are assumed to interact. The monotonic-surface model provides also a multivariate extension of the monotonic likelihood ratio test. This test is discussed here and an approach based on permutations to assess the p-value is proposed. Finally, we combine both ideas (reduced monotonic regression and monotonic-surface estimation) to a simple and easy to interpret model, which leads to a combination of the predictors in a few constant risk groups. Despite the fact that the proposed approach becomes somewhat cumbersome due to the lack of asymptotic methods to infer, it is attractive because of its simplicity and stability. An application will outline the benefit of using bivariate step functions in modelling.