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首页> 外文期刊>Journal of the royal statistical society >Multi-dimensional penalized hazard model with continuous covariates: applications for studying trends and social inequalities in cancer survival
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Multi-dimensional penalized hazard model with continuous covariates: applications for studying trends and social inequalities in cancer survival

机译：具有连续协变量的多维惩罚性风险模型：在研究癌症生存趋势和社会不平等方面的应用

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摘要

Describing the dynamics of patient mortality hazard is a major concern for cancer epidemiologists. In addition to time and age, other continuous covariates have often to be included in the model. For example, survival trend analyses and socio-economic studies deal respectively with the year of diagnosis and a deprivation index. Taking advantage of a recent theoretical framework for general smooth models, the paper proposes a penalized approach to hazard and excess hazard models in time-to-event analyses. The baseline hazard and the functional forms of the covariates were specified by using penalized natural cubic regression splines with associated quadratic penalties. Interactions between continuous covariates and time-dependent effects were dealt with by forming a tensor product smooth. The smoothing parameters were estimated by optimizing either the Laplace approximate marginal likelihood criterion or the likelihood cross-validation criterion. The regression parameters were estimated by direct maximization of the penalized likelihood of the survival model, which avoids data augmentation and the Poisson likelihood approach. The implementation proposed was evaluated on simulations and applied to real data. It was found to be numerically stable, efficient and useful for choosing the appropriate degree of complexity in overall survival and net survival contexts; moreover, it simplified the model building process.
机译：描述患者死亡危险的动态是癌症流行病学家的主要关注点。除了时间和年龄，模型中通常还必须包含其他连续协变量。例如，生存趋势分析和社会经济研究分别涉及诊断年份和剥夺指数。利用最近的一般平滑模型的理论框架，本文提出了一种在事件发生时间分析中对危险和超额危险模型进行惩罚的方法。基线风险和协变量的功能形式通过使用带有相关二次惩罚的惩罚自然立方回归样条指定。通过形成张量积平滑处理连续协变量和时间依赖性效应之间的相互作用。通过优化拉普拉斯近似边际似然准则或似然交叉验证准则来估计平滑参数。通过直接最大化生存模型的受罚可能性来估计回归参数，这避免了数据扩充和泊松可能性方法。拟议的实施方案已在仿真中进行了评估，并应用于实际数据。人们发现它在数值上是稳定的，有效的，对于在整体生存和净生存环境中选择适当程度的复杂性很有用；此外，它简化了模型构建过程。

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