STATISTICAL METHODS FOR SPARSE DEPENDENT DATA

稀疏相关数据的统计方法

• 批准号: 6617826
• 负责人: JOHN J HANFELT
• 金额: $ 11.4万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-16 至 2005-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6617826
• 关键词: computer data analysis; family genetics; human data; method development; statistics /biometry

DESCRIPTION (Applicant's abstract): In medical or epidemiological investigations of complex diseases, often it is desired to assess the aggregation of disease within clusters. For example, a finding of familial aggregation might suggest a genetic component in the etiology of the disease. Unfortunately, such investigations are hampered by the many confounding factors associated with complex diseases, such as demographic, cultural and socioeconomic factors. The study of disease aggregation, with adjustment for many confounding factors, gives rise to sparse dependent data. New statistical methods are necessary to analyze such data. The long-term objective of this research is to develop novel statistical methods that are suitable for sparse dependent data. Special attention is given to genetic epidemiological studies of familial aggregation of disease. The specific aims are to: (1) develop estimating functions that provide inferences for sparse, dependent binary data with proper adjustment for mode of ascertainment of the cluster; (2) develop the theory and application of a general conditional estimating function approach that is valid for various types of sparse dependent data, e.g., discrete data, continuous data or age of onset data; (3) develop approximate likelihood methods to accompany these estimating functions that provide better confidence intervals than the usual Wald confidence interval; (4) evaluate the robustness and efficiency of these methods compared to random-effects methods that require more modeling assumptions; and (5) use the novel statistical methods to reanalyze three data sets involving the aggregation of schizophrenia, obsessive-compulsive disorder, and hypertension, respectively. This research will provide new, more powerful methods to assess aggregation of disease within clusters with proper adjustment for many confounding factors and ascertainment bias.

描述（申请人摘要）：医学或流行病学 在复杂疾病的调查中，通常需要评估 疾病在集群中的聚集。例如，一项发现， 聚集可能表明疾病病因中的遗传成分。 不幸的是，这种调查受到许多混杂因素的阻碍， 与复杂疾病有关的疾病，如人口、文化和 社会经济因素。疾病聚集性研究，包括调整 许多混杂因素导致稀疏相关数据。 需要新的统计方法来分析这些数据。长期 本研究的目的是开发新的统计方法， 适用于稀疏相关数据。特别注意遗传 疾病家族聚集性的流行病学研究。具体目标 是：（1）开发估计函数，为稀疏， 相关的二进制数据，并适当调整确定模式， 集群;（2）发展理论和应用的一般条件 估计函数的方法，是有效的各种类型的稀疏 相关数据，例如，离散数据、连续数据或发病年龄数据;（3） 开发近似似然方法，以配合这些估计功能 比通常的Wald置信度提供更好的置信区间 （4）评价这些方法的鲁棒性和效率， 需要更多建模假设的随机效应方法;（5）使用 新的统计方法重新分析三个数据集， 精神分裂症、强迫症和高血压的聚集， 分别这项研究将提供新的，更强大的方法来评估 疾病聚集在集群内，并对许多疾病进行适当调整 混杂因素和确定偏倚。