The University of Sheffield, Sheffield, UK.
The University of York, York, UK.
Med Decis Making. 2019 Oct;39(7):867-878. doi: 10.1177/0272989X19873661. Epub 2019 Sep 26.
Parametric modeling of survival data is important, and reimbursement decisions may depend on the selected distribution. Accurate predictions require sufficiently flexible models to describe adequately the temporal evolution of the hazard function. A rich class of models is available among the framework of generalized linear models (GLMs) and its extensions, but these models are rarely applied to survival data. This article describes the theoretical properties of these more flexible models and compares their performance to standard survival models in a reproducible case study. We describe how survival data may be analyzed with GLMs and their extensions: fractional polynomials, spline models, generalized additive models, generalized linear mixed (frailty) models, and dynamic survival models. For each, we provide a comparison of the strengths and limitations of these approaches. For the case study, we compare within-sample fit, the plausibility of extrapolations, and extrapolation performance based on data splitting. Viewing standard survival models as GLMs shows that many impose a restrictive assumption of linearity. For the case study, GLMs provided better within-sample fit and more plausible extrapolations. However, they did not improve extrapolation performance. We also provide guidance to aid in choosing between the different approaches based on GLMs and their extensions. The use of GLMs for parametric survival analysis can outperform standard parametric survival models, although the improvements were modest in our case study. This approach is currently seldom used. We provide guidance on both implementing these models and choosing between them. The reproducible case study will help to increase uptake of these models.
生存数据的参数建模很重要，而报销决策可能取决于所选的分布。准确的预测需要足够灵活的模型来充分描述危险函数的时间演变。广义线性模型 (GLM) 及其扩展框架中提供了丰富的模型类，但这些模型很少应用于生存数据。本文描述了这些更灵活模型的理论性质，并在可重现的案例研究中比较了它们与标准生存模型的性能。我们描述了如何使用 GLM 及其扩展（分数多项式、样条模型、广义加性模型、广义线性混合（脆弱性）模型和动态生存模型）来分析生存数据。对于每种方法，我们都提供了对这些方法的优缺点的比较。对于案例研究，我们比较了样本内拟合、外推的合理性以及基于数据分割的外推性能。将标准生存模型视为 GLM 表明，许多模型都强加了线性的限制性假设。对于案例研究，GLM 提供了更好的样本内拟合和更合理的外推。然而，它们并没有提高外推性能。我们还根据 GLM 及其扩展为不同方法的选择提供了指导。对于参数生存分析，GLM 的使用可以优于标准参数生存模型，尽管在我们的案例研究中改进是适度的。这种方法目前很少使用。我们提供了关于实施这些模型和选择它们之间的指导。可重现的案例研究将有助于提高这些模型的采用率。
