Modeling correlated survival data in genetic and biomedical research

对遗传和生物医学研究中的相关生存数据进行建模

• 批准号: 371511-2009
• 负责人: Choi, YunHee
• 金额: $ 1.31万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=528240
• 关键词: Modeling; correlated; survival; data; genetic

Correlated data arise in many forms in genetic and biomedical problems, such as from studies of pairs of eyes, repeated asthma attacks, or families carrying a disease gene. In order to better understand the disease process we are studying, we need to develop new statistical models that can handle these relationships in our data. The recent progress in genetics, genomics, and other 'omics' research has created new types of correlated data that cannot be analyzed using the current methods. Two important features of these new types of data include how the patients or families were picked as well as how strong or weak the similarity is within families or pairs of eyes or asthma episodes. A statistical method that can handle this type of correlation or similarity is called a frailty model. It can be used to understand and control different types of correlation, such as a common gene, family relationships or specific environmental effects like particular foods or supplements.

在遗传和生物医学问题中，相关数据以多种形式出现，例如对眼睛的研究，反复哮喘发作或携带疾病基因的家庭。为了更好地理解我们正在研究的疾病过程，我们需要开发新的统计模型，以处理我们数据中的这些关系。遗传学、基因组学和其他“组学”研究的最新进展创造了新类型的相关数据，这些数据无法使用当前方法进行分析。 这些新类型数据的两个重要特征包括患者或家庭是如何挑选的，以及家庭或眼睛或哮喘发作之间的相似性有多强或多弱。 可以处理这种类型的相关性或相似性的统计方法称为脆弱性模型。它可以用来理解和控制不同类型的相关性，例如共同的基因，家庭关系或特定的环境影响，如特定的食物或补充剂。