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

当观测过程既不完全随机也不完全确定时，纵向数据的半参数建模

• 批准号: 356042-2008
• 负责人: Pullenayegum, Eleanor
• 金额: $ 0.87万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=483015
• 关键词: Semi; parametric; modelling; longitudinal; data

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.

随着时间的流逝，许多医疗保健研究的研究跟随患者。这使研究人员能够评估例如，治疗在几个月或几年的时间内如何影响患者的健康，而不是仅仅研究对治疗的最初反应。大多数涉及随着时间的关注的患者的研究都涉及在医生看到患者或每次访问中收集的信息的时代的某些不可预测性。即使研究人员提出了一条时间表，指定何时访问，我错过了预定的访问，在不适的情况下更频繁地去看医生，也要求在特定访问时不接受测试或测量，或者完全退出研究。每次访问时收集的访问时间和信息的这些变化很少是完全随机的，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，则分析的结果可能非常误导。该提案中概述的工作将开发分析这种不规则数据的方法，重点是为重要的医疗保健问题提供精确的，公正的答案。