STATISTICAL METHODS FOR SPARSE DEPENDENT DATA

描述(申请人摘要)：医学或流行病学 对于复杂疾病的调查，通常需要评估 聚集性聚集性疾病。例如，一个家族性的发现 聚集可能暗示了这种疾病的病因学中的遗传成分。 不幸的是，这种调查受到许多混杂因素的阻碍。 与复杂疾病相关的，如人口、文化和 社会经济因素。疾病聚集的研究，调整为 许多混杂因素，导致了稀疏的相依数据。 分析这些数据需要新的统计方法。长期的 这项研究的目标是开发新的统计方法，这些方法 适用于稀疏相关数据。特别关注的是基因 疾病家族聚集性的流行病学研究。具体目标 目的是：(1)开发为稀疏提供推理的估计函数， 具有确定模式的适当调整的相依二进制数据 (2)发展了一般条件集的理论和应用 一种适用于各种稀疏类型的估计函数方法 相依数据，例如离散数据、连续数据或发病年龄数据； 开发近似似然方法来伴随这些估计函数 它们提供了比通常的Wald置信度更好的置信度 对这些方法的稳健性和效率进行了比较 到需要更多建模假设的随机效果方法；以及(5)使用 新的统计方法重新分析三个数据集，涉及 精神分裂症、强迫症和高血压的聚集物， 分别进行了分析。这项研究将提供新的、更强大的评估方法 在集群内聚集疾病，并对许多疾病进行适当调整 混杂因素和确定偏差。

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.