Time-dependent Robust Joint Modelling: Analysing a wealth of longitudinal outliers

瞬态鲁棒联合建模：分析大量纵向异常值

• 批准号: EP/P026028/1
• 负责人: Lisa McFetridge
• 金额: $ 12.8万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP026028%2F1
• 关键词: Time; dependent; Robust; Joint; Modelling

Joint modelling is a sophisticated technique that allows one to simultaneously analyse the evolution, over time, of repeated measurements from individuals and the impact this has on the time to a particular event of interest. Commonly, it is applied to medical applications where patients are observed over time with the aim of investigating how and why their responses change to treatment and how this affects their survival. From this, it is evident that such approaches can be applied to a vast array of research questions, from cancer research to the analysis of chronic diseases such as heart disease, diabetes, stroke, to name but a few. As a result of this advantage, the volume of research publications utilising joint models has exploded in the last few decades. Despite this, however, only limited research efforts have been directed at investigating one of the key assumptions of these models: that the random terms within these models follow normal distributional assumptions. This prevailing assumption of normality is detrimentally impacted when longitudinal outliers are present. Simple removal of these outliers will not only reduce sample size but, more importantly, would exclude important cases which commonly guide innovation in biomedical sciences; it is typically the analysis of outlying cases which tell us more about disease progression. Instead, this research will advance robust joint modelling techniques which both restrict the impact of outliers, providing more accurate and precise estimates to be obtained, and allow a high level of precision in the identification of such outliers for further exploration.However, this research area is in its infancy with the volume of work to date on robust joint modelling being currently somewhat limited. This is due to the potentially restrictive assumptions of the current methodology for these models i.e. that the impact of outliers is constant, unchanging over time. There are no established theoretical tools for handling such a situation, an undesirable situation that will be rectified through this research. To do so, I will develop a novel methodology, the time-varying outlier impacts (TOI) approach, which will allow the degree at which outliers are down weighed to change over time. Doing so, will allow more realistic scenarios to be modelled using such techniques, for example, modelling patients reaction to starting a new treatment, accounting for the fact that it will take time for them to adjust to the new treatment, which could result in outlying measurements being taken from such patients or all measurements taken from the patient outlying from the trends of the population.Another reason for limited research utilising robust joint modelling techniques is the lack of available software to fit such models. It has only been in recent years, since the introduction of the JM software package in R in 2008, that software has become available to fit standard joint models. Each of these joint modelling software packages have normal distributional assumptions for the random terms and thus cannot handle the analysis of data which contains longitudinal outliers, providing biased and imprecise estimates in the presence of outliers. This issue will also be alleviated through the work undertaken in this project through the development of a software package in R for robust joint modelling that will utilise the newly developed TOI approach.

联合建模是一种复杂的技术，它使人们能够同时分析个人重复测量随时间的演变以及这对特定感兴趣事件的时间的影响。通常，它被应用于医疗应用，其中随着时间的推移观察患者，目的是调查他们对治疗的反应如何以及为什么会改变，以及这如何影响他们的生存。由此可见，这种方法可以应用于大量的研究问题，从癌症研究到心脏病、糖尿病、中风等慢性疾病的分析，仅举几例。由于这一优势，在过去的几十年里，利用联合模型的研究出版物数量激增。尽管如此，然而，只有有限的研究工作一直针对调查这些模型的关键假设之一：这些模型中的随机项遵循正态分布假设。当存在纵向离群值时，这种普遍的正态性假设会受到负面影响。简单地去除这些离群值不仅会减少样本量，更重要的是，会排除通常指导生物医学科学创新的重要病例;通常是对离群病例的分析告诉我们更多关于疾病进展的信息。相反，这项研究将推进强大的联合建模技术，既限制了异常值的影响，提供更准确和精确的估计，以获得，并允许高精度的识别这种异常值的进一步exploration.However，这一研究领域是在其起步阶段的工作量到目前为止，强大的联合建模目前有些有限。这是由于这些模型的当前方法的潜在限制性假设，即离群值的影响是恒定的，不随时间变化。没有既定的理论工具来处理这种情况，这是一种不良的情况，将通过这项研究加以纠正。为此，我将开发一种新的方法，即时变离群值影响（TOI）方法，该方法允许离群值的权重随时间变化。这样做，将允许使用这些技术模拟更现实的场景，例如，模拟患者对开始新治疗的反应，考虑到他们需要时间来适应新治疗，这可能导致从这些患者中获取的测量值偏离人群趋势，或者从患者中获取的所有测量值偏离人群趋势。可靠的联合建模技术是缺乏适用于这种模型的软件。直到最近几年，自2008年在R中引入JM软件包以来，该软件才可用于拟合标准关节模型。这些联合建模软件包中的每一个都对随机项进行了正态分布假设，因此无法处理包含纵向离群值的数据分析，从而在存在离群值的情况下提供有偏差和不精确的估计。这个问题也将通过在该项目中开展的工作得到缓解，通过开发R软件包进行强大的联合建模，将利用新开发的TOI方法。

项目成果

Robust joint modelling: A new approach to handle time-varying outlier impacts

鲁棒联合建模：处理时变异常影响的新方法

• 发表时间: 2019
• 作者: Boyle L

Longitudinal and survival analysis methods for modelling healthcare applications

用于建模医疗保健应用的纵向和生存分析方法

• 发表时间: 2018
• 作者: Boyle L

Time-varying outlier impacts on robust mixed models with an application in renal research

时变异常值对稳健混合模型的影响及其在肾脏研究中的应用

• 发表时间: 2018
• 作者: Boyle L

The impact of time-varying outliers on mixed effects models: a simulation study motivated by renal data

时变异常值对混合效应模型的影响：基于肾脏数据的模拟研究

• 发表时间: 2018
• 作者: Boyle L

Robust joint modelling of longitudinal and survival data: Incorporating a time-varying degrees-of-freedom parameter.

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
• 发表时间: 2021
• 期刊: Biometrical journal. Biometrische Zeitschrift
• 作者: McFetridge LM