Everything Changes Over Time: Transforming Joint Modelling Methodology

临床医生通常在治疗疾病的整个过程中定期收集患者的数据。这种纵向数据为患者提供了关于事物如何随时间变化的有价值的见解-例如，跟踪疾病的进展，患者对特定治疗的反应，干预策略的有用性。通常，此类数据将与关键事件信息一起收集，例如患者的至恢复时间、至复发时间或至死亡时间。联合模型使这种生存和纵向数据之间的关系，以数学表示，经常连接一个线性混合效应模型的考克斯比例风险model.Despite在这一领域的研究在最近几年的显着增长，需要更广泛的模型，真正代表随着时间的推移自然的生物变化。随着联合模型于1996年首次推出，这个相对年轻的研究领域有很多机会可以探索新的方法。该项目将解决一个这样的研究途径-联合建模方法的转换，以便更好地表示随着时间的推移而变化的效果，并处理并非所有患者对治疗的反应与人口相同的常见情况。为此，该研究将在稳健的线性混合效应模型中加入随机成分，以表示纵向过程。这将准确地模拟个体自身平均纵向响应随时间的波动，同时降低纵向离群值的负面影响，这是联合模型设置中的新研究。通过这样做，这将更好地代表个体生物标志物如何随时间变化的真实纵向过程，从而影响其生存，提供更精确的解释和动态预测。

Clinicians typically collect data from patients regularly throughout the treatment of an illness. Such longitudinal data provides valuable insights into how things change over time for patients - tracking the progression of the disease, a patient's reaction to particular treatments, the usefulness of intervention strategies, for example. It is common that such data will be collected alongside key event information such as the time to recovery, time to relapse or time to death of patients. Joint models enable the relationships between this survival and longitudinal data to be mathematically represented, frequently linking a linear mixed effects model to a Cox proportional hazards model.Despite the significant growth in this field of research in recent years, a wider array of models is needed to truly represent natural biological changes over time. With joint models being first introduced in 1996, this relatively young field of research has many opportunities in which novel approaches can be explored. This project will tackle one such avenue of research - the transformation of joint modelling methodology to both allow a better representation of changing effects over time and to handle the common situation where not all patients will react the same to treatments as the population.To do so, this research would incorporate a stochastic component within a robust linear mixed effects models to represent the longitudinal process. This would accurately model fluctuations in an individual's own average longitudinal response over time whilst down weighing the negative impact of longitudinal outliers, novel research within a joint model setting. In doing so, this would better represent the true longitudinal process of how individuals' biomarkers, for example, change over time and thus impact their survival, providing more precise interpretations and dynamic predictions.