Semi-parametric modelling of longitudinal data when the observation process is neither completely random nor completely deterministic

医疗保健研究中的许多研究随着时间的推移对患者进行了跟踪调查。这使得研究人员可以评估，例如，治疗在几个月或几年内如何影响患者的健康，而不仅仅是看治疗的最初反应。大多数涉及长期跟踪患者的研究都涉及医生看患者的时间或每次就诊时收集的信息中的一些不可预测性。即使研究人员列出了具体应该在何时进行探视的时间表，患者也可能错过预定的探视，在身体不适时更频繁地去看医生，要求在特定探视时不进行测试或测量，或者完全退出研究。就诊时间和每次就诊时收集的信息的这些变化很少是完全随机的，而且往往与患者的健康有关。如果在分析数据时忽略这一点，分析结果可能会非常具有误导性。该提案中概述的工作将开发分析这类不规则数据的方法，重点是为重要的医疗保健问题提供准确、公正的答案。

Many studies in healthcare research follow patients over time. This allows researchers to evaluate how, for example, treatment impacts a patient's health over a number of months or years, rather than looking at just the initial response to treatment. Most research involving patients who are followed over time involves some unpredictability in the times when patients are seen by their physicians, or in the information that is gathered at each visit. Even if the researcher lays out a timeline specifying when visits should occur, patients my miss a scheduled visit, see their doctor more often when unwell, ask that a test or measurement not be taken at a particular visit, or withdraw from the study altogether. These variations in visit times and information collected at each visit are rarely completely random, and are often associated with the patient's health. If this is ignored when analysing the data, the results of the analysis can be very misleading. Work outlined in this proposal will develop methods to analyse this type of irregular data, with a focus on providing precise, unbiased answers to important healthcare questions.