Causal Modeling of Recurrent Injury Data

复发性损伤数据的因果建模

• 批准号: 478521-2015
• 负责人: Steele, Russell
• 金额: $ 6.22万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=579579
• 关键词: Causal; Modeling; Recurrent; Injury; Data

The analysis of the time till medical events repeatedly observed on individual subjects presents unique challenges, particularly when trying to account for potentially important sources of bias. These biases result from several sources, many of which are not addressed in standard statistical practices in various subfields of medicine. We will develop new statistical methods both for datasets involving injury and re-injury of athletes and in perinatal epidemiology, where the repeated events include spontaneous abortion, miscarriage, and fetal death, as well as pregnancy (e.g. infertility patients).

对个体受试者重复观察到的医疗事件的时间进行分析提出了独特的挑战， 特别是在试图解释潜在的重要偏见来源时。这些偏见来自几个来源，许多 这些问题在医学各子领域的标准统计实践中没有得到解决。我们将开发新的统计 方法既适用于涉及运动员受伤和再受伤的数据集，也适用于围产期流行病学， 包括自然流产、流产和胎儿死亡，以及怀孕（例如不孕症患者）。