Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects
Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects
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DOI：
10.1002/sim.1203
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发表时间：
2002-08-15
影响因子：
2
通讯作者：
Parmar, MKB
中科院分区：
文献类型：
--
作者：
Royston, P;Parmar, MKB
删失生存数据的建模几乎总是通过考克斯比例风险回归进行的。然而，对这些数据使用参数模型可能具有一些优点。例如，非比例风险，一个潜在的困难与考克斯模型，有时可以用一种简单的方式处理，和可视化的风险函数是容易得多。Weibull和对数逻辑模型的扩展，提出了自然三次样条平滑的基线日志累积风险和日志累积几率的故障函数。进一步的扩展，允许非比例效应的一些或所有的协变量。提出了协变量效应（如治疗）所选尺度适当性的假设检验。新模型应用于癌症的两个数据集。结果抛出有趣的光的行为的风险函数和风险比随着时间的推移。这里描述的工具可能是一个步骤，提供更深入的了解疾病的自然史和临床事件的可能的根本原因。我们通过使用癌症中的两个例子来说明这些方面。版权所有（C）2002约翰威利父子有限公司
Modelling of censored survival data is almost always done by Cox proportional-hazards regression. However, use of parametric models for such data may have some advantages. For example, nonproportional hazards, a potential difficulty with Cox models, may sometimes be handled in a simple way, and visualization of the hazard function is much easier. Extensions of the Weibull and log-logistic models are proposed in which natural cubic splines are used to smooth the baseline log cumulative hazard and log cumulative odds of failure functions. Further extensions to allow non-proportional effects of some or all of the covariates are introduced. A hypothesis test of the appropriateness of the scale chosen for covariate effects (such as of treatment) is proposed. The new models are applied to two data sets in cancer. The results throw interesting light on the behaviour of both the hazard function and the hazard ratio over time. The tools described here may be a step towards providing greater insight into the natural history of the disease and into possible underlying causes of clinical events. We illustrate these aspects by using the two examples in cancer. Copyright (C) 2002 John Wiley Sons, Ltd.