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

许多医疗保健研究对患者进行了长期跟踪。这使得研究人员能够评估，例如，治疗在几个月或几年的时间里如何影响病人的健康，而不仅仅是观察治疗的最初反应。大多数涉及长期跟踪患者的研究都涉及到一些不可预测性，比如患者看医生的时间，或者每次就诊时收集到的信息。即使研究人员制定了一个时间表，规定何时应该进行访问，患者也可能会错过预定的访问，在身体不适时更频繁地去看医生，要求在特定的访问中不要进行测试或测量，或者完全退出研究。这些访问时间的变化和每次访问收集的信息很少是完全随机的，并且通常与患者的健康有关。如果在分析数据时忽略了这一点，那么分析的结果可能非常具有误导性。本提案中概述的工作将制定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.