论文标题
使用序数预测变量的贝叶斯混合模型用于更改点估计
A Bayesian Mixture Model for Changepoint Estimation Using Ordinal Predictors
论文作者
论文摘要
在回归模型中，在医疗环境中通常可以看到具有固有排序的预测变量，例如肿瘤分期范围和ECOG性能状态。从统计上讲，可能很难确定序数预测变量的功能形式。通常，这种变量是根据其高于或低于某个临界值的二分法。其他方法方便地将顺序预测变量视为连续变量，并与结果保持线性关系。但是，任意选择一种方法可能导致推理和治疗不准确。在本文中，我们提出了一个贝叶斯混合模型，通过考虑通过阈值检测问题的镜头来考虑变更点，同时评估回归模型中预测变量的适当形式。通过使用混合模型框架来考虑变量的二分法和线性形式，估计值是线性和二进制参数化的加权平均值。该方法适用于连续，二元和生存结果，并且很容易被惩罚回归。我们使用仿真研究评估了提出的方法，并将其应用于两个实际数据集。我们提供JAGS代码以方便实施。
In regression models, predictor variables with inherent ordering, such as tumor staging ranging and ECOG performance status, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to simultaneously assess the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. By using a mixture model framework to consider both dichotomous and linear forms for the variable, the estimate is a weighted average of linear and binary parameterizations. This method is applicable to continuous, binary, and survival outcomes, and easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation.
