Robust joint modelling of longitudinal and survival data: Incorporating a time-varying degrees-of-freedom parameter.
Robust joint modelling of longitudinal and survival data: Incorporating a time-varying degrees-of-freedom parameter.
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纵向和生存数据的鲁棒联合建模：结合时变自由度参数。
DOI：
10.1002/bimj.202000253
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
McFetridge LM
中科院分区：
文献类型：
--
作者：
McFetridge LM
个体生物标志物的监测有可能解释生存结局的风险。在实践中，这些测量是间歇性地观察到的，并且已知会受到相当大的测量误差的影响。纵向和生存数据的联合建模使我们能够将间歇性测量的易出错生物标志物与生存结局风险相关联，从而在医学数据分析中发挥重要作用。文献中的大多数联合模型都是基于高斯假设建立的。这使得它们对异常值敏感。在这项工作中，我们研究了一系列强大的模型来解决这个问题。特别令人感兴趣的是，在医疗数据中，离群值可能随着时间的推移以不同的频率出现，例如，在患者适应治疗变化的时期。受原发性胆汁性肝硬化患者数据分析的启发，引入了一种具有时变稳健性的新模型。通过激励性的例子和模拟研究，本研究不仅强调了在医学数据分析和联合建模研究中考虑纵向离群值的必要性，而且还强调了不正确估计自由度参数的偏差和效率低下。这项工作提出了一些方法，除了随时间变化的鲁棒性，每种方法都可以使用Rpackagerobjm拟合。
Monitoring of individual biomarkers has the potential of explaining the hazard of survival outcomes. In practice, these measurements are intermittently observed and are known to be subject to substantial measurement error. Joint modelling of longitudinal and survival data enables us to associate intermittently measured error‐prone biomarkers with risks of survival outcomes and thus plays an important role in the analysis of medical data. Most of the joint models available in the literature have been built on the Gaussian assumption. This makes them sensitive to outliers. In this work, we study a range of robust models to address this issue. Of particular interest is the common occurrence in medical data that outliers can occur with different frequencies over time, for example, in the period when patients adjust to treatment changes. Motivated by the analysis of data gathered from patients with primary biliary cirrhosis, a new model with a time‐varying robustness is introduced. Through both the motivating example and a simulation study, this research not only stresses the need to account for longitudinal outliers in the analysis of medical data and in joint modelling research but also highlights the bias and inefficiency from not properly estimating the degrees‐of‐freedom parameter. This work presents a number of methods in addition to the time‐varying robustness, and each method can be fitted using theRpackagerobjm.