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基本説明
A practical guide for handling missing data, this book provides a flexible and accessible framework for multiple imputation along with strategies for obtaining effective solutions to these problems.
Full Description
Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science-multiple imputation-fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise. Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author's package MICE is included throughout the book.Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences:(Bio)statisticians, epidemiologists, and methodologists in the social and health sciencesSubstantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipesThis graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.
Contents
BasicsIntroductionThe problem of missing dataConcepts of MCAR, MAR and MNARSimple solutions that do not (always) workMultiple imputation in a nutshellGoal of the bookWhat the book does not coverStructure of the bookExercisesMultiple imputationHistoric overviewIncomplete data conceptsWhy and when multiple imputation worksStatistical intervals and testsEvaluation criteriaWhen to use multiple imputationHow many imputations?ExercisesUnivariate missing dataHow to generate multiple imputationsImputation under the normal linear normalImputation under non-normal distributionsPredictive mean matchingCategorical dataOther data typesClassification and regression treesMultilevel dataNon-ignorable methodsExercisesMultivariate missing dataMissing data patternIssues in multivariate imputationMonotone data imputationJoint ModelingFully Conditional SpecificationFCS and JMConclusionExercisesImputation in practiceOverview of modeling choicesIgnorable or non-ignorable?Model form and predictorsDerived variablesAlgorithmic optionsDiagnosticsConclusionExercisesAnalysis of imputed dataWhat to do with the imputed data?Parameter poolingStatistical tests for multiple imputationStepwise model selectionConclusionExercisesCase studiesMeasurement issuesToo many columnsSensitivity analysisCorrect prevalence estimates from self-reported dataEnhancing comparabilityExercisesSelection issuesCorrecting for selective drop-outCorrecting for non-responseExercisesLongitudinal dataLong and wide formatSE Fireworks Disaster StudyTime raster imputationConclusionExercisesExtensionsConclusionSome dangers, some do's and some don'tsReportingOther applicationsFuture developmentsExercisesAppendices: SoftwareRS-PlusStataSASSPSSOther softwareReferencesAuthor IndexSubject Index
