Multiple Imputation and its Application
Multiple Imputation and its Application
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发表时间：
2013-02
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通讯作者：
J. Carpenter;M. Kenward
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作者：
J. Carpenter;M. Kenward
分析部分观测数据的实用指南。从数据中收集、分析和推断是医学和社会科学研究的核心。不幸的是，收集所有预期数据的可能性很小。关于从由此产生的不完整数据中推断的文献现在是巨大的，并且随着针对大型和复杂数据结构的方法的开发以及随着计算机能力和合适的软件的增加使研究人员能够应用这些方法而继续增长。这本书集中在一个特定的统计方法分析和不完整的数据，称为多重插补（MI）的推论。多元智能之所以有吸引力，是因为它既实用又适用广泛。作者的目的是澄清缺失数据引起的问题，描述MI的基本原理，各种插补模型和相关算法之间的关系及其在日益复杂的数据结构中的应用。多重插补及其应用：讨论了部分观测数据分析所提出的问题，以及分析所依据的假设。提出了一个实用的指南，分析观察性研究和随机试验的不完整数据时要考虑的问题。提供了一个详细的讨论实际使用MI与现实世界的例子来自医疗和社会统计。探索使用多重插补、生存分析、多水平多重插补、通过多重插补的敏感性分析、使用多重插补和双重稳健多重插补的无应答权重处理非线性关系和相互作用。多重插补及其应用是针对定量研究人员和学生在医学和社会科学的目的是澄清所提出的问题，分析不完整的数据数据，概述了MI的基本原理，并描述如何考虑和解决在其应用中出现的问题。
A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation. Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.