Novel Statistical Methods for Data with Missing Values

缺失值数据的新统计方法

• 批准号: 7072231
• 负责人: HUA YUN CHEN
• 金额: $ 15.72万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7072231
• 关键词: animal data; clinical research; computer program /software; computer simulation; computer system design /evaluation; data collection methodology /evaluation; human data; mathematical model; method development; statistics /biometry

DESCRIPTION (provided by applicant): Missing covariate values are common in studies of risk factors of diseases and in many other biomedical studies. Simple complete-case analysis which is routinely used suffers from bias in addition to efficiency loss. Current advanced statistical methods for analyzing such data have limited usage in practice because of the robust concern, or the difficulty in implementation, or both. This project aims at developing new statistical methods for modeling missing covariates in regression models to make inferences on regression parameters with missing covariates robust, efficient, and easy to implement. The objective is to be reached through four steps: (1) A general semi-parametric odds ratio model is proposed for complex missing data problems. The proposed model makes the likelihood approach commonly used in practice more robust and flexible, and easy to apply. (2) The likelihood method for regression with missing data is further robustified in three ways. When missing patterns are relatively simple, smoothing spline models for odds ratio function is proposed; When missing patterns are complex, likelihood estimator is modified to be doubly robust and locally efficient; A framework is proposed for sensitivity analysis with general missing data mechanisms. (3) For problems with a large number of covariates subject to missing values, model selection procedures are studied based on imputed complete data under the semiparametric covariate model. Such procedures can be very helpful in studying risk factors of health events, such as in identifying risk factors of bone fracture from a set of potential risk factors subject to missing values. (4) For all the missing data problems under consideration, software for implementing methods of the research outcomes will be developed and disseminated. The proposed research, when completed, will make analyses of biomedical data with missing covariate values more accessible to researchers in many applied fields and thus promote efficient use of valuable data, such as those from HIV and cancer studies.

描述（由申请人提供）：在疾病危险因素的研究和许多其他生物医学研究中，协变量值缺失是常见的。常规使用的简单全案例分析除了效率损失外，还存在偏差。目前用于分析这类数据的先进统计方法在实践中使用有限，因为存在强大的担忧，或者执行困难，或者两者兼而有之。本项目旨在发展回归模型中缺失协变量建模的新统计方法，使缺失协变量对回归参数的推断稳健、高效、易于实现。目标是通过四个步骤来实现的：(1)针对复杂的缺失数据问题提出了一般的半参数比值比模型。该模型使实践中常用的似然方法具有更强的鲁棒性和灵活性，易于应用。(2)对缺失数据回归的似然方法进行了三方面的进一步鲁棒性验证。当缺失模式相对简单时，采用平滑样条模型求解比值比函数；当缺失模式比较复杂时，对似然估计进行了改进，使其具有双重鲁棒性和局部有效性；提出了一个具有一般缺失数据机制的敏感性分析框架。(3)针对协变量数量较多且存在缺失值的问题，研究了半参数协变量模型下基于输入完整数据的模型选择过程。这些程序对于研究健康事件的风险因素非常有帮助，例如从一组可能存在缺失值的潜在风险因素中识别骨折的风险因素。(4)针对所考虑的所有缺失数据问题，将开发和传播用于研究成果实施方法的软件。拟议的研究完成后，将使许多应用领域的研究人员更容易获得对缺少协变量值的生物医学数据的分析，从而促进有效利用有价值的数据，例如来自艾滋病毒和癌症研究的数据。