GLM, GLMM, GEE for Correlated Discrete Data with Over-dispersion, Zero-inflation, Measurement Error and Misspecification

GLM、GLMM、GEE，用于具有过度离散、零膨胀、测量误差和错误指定的相关离散数据

• 批准号: 8593-2013
• 负责人: Paul, Sudhir
• 金额: $ 1.38万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=592934
• 关键词: GLM; GLMM; GEE; Correlated; Discrete

The main area of my research is methodological development of statistical procedures in the general area of biostatistics where longitudinal or clustered correlated data arise in the form of counts, proportions or survival times. Longitudinal data arise, for example, in medical studies, where patients are observed over time for survival after a medical procedure. Clustered data arise in many situations as groups of responses. For example, in family studies, family members (mother and her children or siblings in the same family) will have similar medical problems. So, the responses of members of the same family will be correlated. One aim in such studies is to establish the relationship between a response variable (disease status) and covariates or regression variables (age, ethnicity, presence of other disease of the patient). However, data that arise in practice often have a lot of complications. For example, count data or data in the form of proportions often show over-dispersion (variance is greater than the mean), zero-inflation (more zeros than what can be predicted by a simple model for the analysis of the data). Further, measurement error in explanatory (regression) variables and missing values in both response and explanatory variables are prevalent in many scientific fields. Data of these types arise in fields as diverse as biology, epidemiology, social science and engineering.

我研究的主要领域是生物统计学一般领域中统计程序的方法论发展，其中纵向或聚类相关数据以计数、比例或生存时间的形式出现。例如，纵向数据出现在医学研究中，在医学研究中，随着时间的推移观察患者在接受医疗手术后的生存情况。聚类数据在许多情况下作为响应组出现。例如，在家庭研究中，家庭成员（母亲和她的孩子或同一家庭的兄弟姐妹）都会有类似的医疗问题。因此，同一家庭成员的反应将是相关的。此类研究的目的之一是建立响应变量（疾病状态）与协变量或回归变量（患者的年龄、种族、是否存在其他疾病）之间的关系。然而，实践中出现的数据往往有很多复杂性。例如，计数数据或比例形式的数据通常表现出过度离散（方差大于平均值）、零膨胀（比用于数据分析的简单模型可以预测的零更多）。此外，解释（回归）变量的测量误差以及响应变量和解释变量的缺失值在许多科学领域都很普遍。这些类型的数据出现在生物学、流行病学、社会科学和工程学等不同领域。