Vasconcelos Julio C S, Travassos Thiago da Costa, Ortega Edwin M M, Cordeiro Gauss M, Oliveira Reis Leonardo
UNIFESP, Universidade Federal de São Paulo, São José dos Campos, Brazil.
Hospital PUCC, Campinas, Brazil.
J Appl Stat. 2023 Jul 20;51(5):1007-1022. doi: 10.1080/02664763.2023.2229973. eCollection 2024.
Several statistical models have been proposed in recent years, among them is the semiparametric regression. In medicine, there are several situations in which it is impracticable to consider a linear regression for statistical modeling, especially when the data contain explanatory variables that present a nonlinear relationship with the response variable. Another common situation is when the response variable does not have a unimodal shape, and it is not possible to adopt distributions belonging to the symmetric or asymmetric classes. In this context, a semiparametric heteroskedastic regression is proposed based on an extension of the normal distribution. Then, we show the usefulness of this model to analyze the cost of prostate cancer surgery. The predictor variables refer to two groups of patients such that one group receives a multimodal local anesthetic solution (Preemptive Target Anesthetic Solution) and the second group is treated with neuraxial blockade (spinal anesthesia/traditional standard). The other relevant predictor variables are also evaluated, thus allowing for the in-depth interpretation of the predictor variables with a nonlinear effect on the dependent variable . The penalized maximum likelihood method is adopted to estimate the model parameters. The new regression is a useful statistical tool for analyzing medical data.
近年来已经提出了几种统计模型，其中包括半参数回归。在医学领域，有几种情况不适合采用线性回归进行统计建模，特别是当数据包含与响应变量呈现非线性关系的解释变量时。另一种常见情况是响应变量不具有单峰形状，并且无法采用属于对称或不对称类别的分布。在此背景下，基于正态分布的扩展提出了一种半参数异方差回归。然后，我们展示了该模型在分析前列腺癌手术成本方面的有用性。预测变量涉及两组患者，一组接受多模式局部麻醉溶液（预先目标麻醉溶液），另一组接受神经轴阻滞（脊髓麻醉/传统标准）治疗。还评估了其他相关预测变量，从而能够深入解释对因变量具有非线性影响的预测变量。采用惩罚最大似然法估计模型参数。这种新的回归是分析医学数据的一种有用的统计工具。
