Semiparametric statistical methods for censored or missing data and their applications in survival analysis and other related areas

截尾或缺失数据的半参数统计方法及其在生存分析和其他相关领域的应用

• 批准号: 261567-2008
• 负责人: Lu, Xuewen
• 金额: $ 1.09万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=479895
• 关键词: Semiparametric; statistical; methods; censored; missing

In medicine, economics, social science, engineering and other fields, practitioners are often faced with problems of exploring the relationship between an outcome variable and a set of covariates for prediction purposes. The standard statistical methods are often not directly available to solve the problems due to censored and missing data. Censored and missing data arise in almost all scientific disciplines. For example, in a clinical trial, the investigators are interested in evaluating the effect of a treatment on survival in HIV-1 seropositive drug users, adjusted for other predictive covariates such as BMI (body mass index) and age. Some patients may be still alive when the study terminates and their survival times are censored. In another example, autopsy for some patients may not be carried out and the failure causes are unknown. In this situation, some values of censoring indicators are missing. In many cases, people just simply delete records for which any data are censored or missing, and conduct the so-called "complete-case analysis". Such an analysis can lead to severe biases and wrong conclusions. Moreover, there may be many covariates and it is difficult to select important ones. For instance, investigators need to select active genes to predict patients' survival in microarray analysis. There is a challenge of high dimensionality of the gene expression data. This proposal is to develop semiparametric models to address all the issues arising from the aforementioned real applications. Semiparametric models are the models that include both a parametric and nonparametric component. Such models allow flexible covariate effects; they play the role of dimension reduction and avoid the curse of dimensionality; estimators in such models are more robust and efficient than in traditional parametric models and pure nonparametric models. Therefore, it is challenging and rewarding to work on semiparametric models. The outcomes of the proposed research will make contributions to the development of statistical theory and help bring the benefits of prosperity to all Canadians. In addition, the research will contribute significantly to the training of highly qualified young Canadian professionals.

在医学、经济学、社会科学、工程学等领域，从业者经常面临探索结果变量与一组协变量之间的关系以进行预测的问题。由于数据的删减和缺失，标准的统计方法往往不能直接用于解决问题。几乎所有的科学学科都会出现数据被删减和丢失的情况。例如，在一项临床试验中，研究人员对评估治疗对HIV-1血清阳性吸毒者生存的影响感兴趣，并根据BMI（身体质量指数）和年龄等其他预测性协变量进行调整。当研究结束时，一些患者可能仍然活着，他们的生存时间被审查。在另一个例子中，一些病人可能没有进行尸检，失败的原因不明。在这种情况下，一些审查指标的值缺失。在很多情况下，人们只是简单地删除任何数据被审查或丢失的记录，并进行所谓的“完整案例分析”。这样的分析可能会导致严重的偏见和错误的结论。此外，协变量可能很多，很难选择重要的变量。例如，研究人员需要在微阵列分析中选择活性基因来预测患者的生存。基因表达数据的高维性是一个挑战。本建议是开发半参数模型来解决上述实际应用中出现的所有问题。半参数模型是同时包含参数和非参数组件的模型。这样的模型允许灵活的协变量效应；它们起到降维的作用，避免了维数的诅咒；与传统的参数模型和纯非参数模型相比，该模型中的估计器具有更强的鲁棒性和效率。因此，研究半参数模型是具有挑战性和有益的。拟议的研究成果将对统计理论的发展作出贡献，并有助于为所有加拿大人带来繁荣的好处。此外，这项研究将大大有助于培训高素质的加拿大年轻专业人员。