Zhang Zhongheng
Department of Critical Care Medicine, Jinhua Municipal Central Hospital, Jinhua Hospital of Zhejiang University, Jinhua 321000, China.
Ann Transl Med. 2016 Dec;4(24):484. doi: 10.21037/atm.2016.08.45.
Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings.
威布尔回归模型是最流行的参数回归模型形式之一，它能提供基线风险函数的估计值以及协变量的系数。由于技术上的困难，与半参数比例风险模型相比，威布尔回归模型在医学文献中很少使用。为了让临床研究人员熟悉威布尔回归模型，本文介绍了一些关于威布尔回归模型的基础知识，然后说明了如何使用R软件拟合该模型。该软件包有助于将估计系数转换为临床相关统计量，如风险比（HR）和事件时间比（ETR）。可以通过检查按分类变量分层的Kaplan-Meier曲线来评估模型的充分性。该软件包提供了另一种对威布尔回归模型进行建模的方法。check.dist()函数有助于评估模型的拟合优度。变量选择基于协变量的重要性，可以使用anova()函数进行检验。或者，从完整模型开始的向后消除法是模型开发的有效方法。模型开发后对威布尔回归模型进行可视化很有意思，它提供了另一种报告研究结果的方式。
