{"product_id":"multiple-imputation-and-its-application-hardback-9781119756088","title":"Multiple Imputation and its Application (Hardback) 9781119756088","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultiple Imputation and its Application\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJames R. Carpenter (Author), Jonathan W. Bartlett (Author), Tim P. Morris (Author), Angela M. Wood (Author), Matteo Quartagno (Author), Michael G. Kenward (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119756088, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 August 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e464 pages\u003cbr\u003e24.4 x 17 x 3.1 cm, 0.794 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eMultiple Imputation and its Application\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThe most up-to-date edition of a bestselling guide to analyzing partially observed data\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn this comprehensively revised Second Edition of \u003ci\u003eMultiple Imputation and its Application,\u003c\/i\u003e a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.  \u003c\/p\u003e\n\u003cp\u003eWith an accessible and carefully structured presentation aimed at quantitative researchers, \u003ci\u003eMultiple Imputation and its Application\u003c\/i\u003e is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets\u003c\/li\u003e \u003cli\u003eCareful discussion of key concepts\u003c\/li\u003e \u003cli\u003eA range of examples illustrating the key ideas\u003c\/li\u003e \u003cli\u003ePractical advice on using multiple imputation\u003c\/li\u003e \u003cli\u003eExercises and examples designed for use in the classroom and\/or private study\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eWritten for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, \u003ci\u003eMultiple Imputation and its Application\u003c\/i\u003e will also earn a place in the libraries of graduate students undertaking quantitative analyses.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface to the second edition xiii\u003c\/p\u003e \u003cp\u003eData acknowledgements xv\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003eGlossary xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Foundations 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Reasons for missing data 5\u003c\/p\u003e \u003cp\u003e1.2 Examples 6\u003c\/p\u003e \u003cp\u003e1.3 Patterns of missing data 7\u003c\/p\u003e \u003cp\u003e1.4 Inferential framework and notation 10\u003c\/p\u003e \u003cp\u003e1.5 Using observed data to inform assumptions about the missingness mechanism 21\u003c\/p\u003e \u003cp\u003e1.6 Implications of missing data mechanisms for regression analyses 24\u003c\/p\u003e \u003cp\u003e1.7 Summary 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Multiple Imputation Procedure and Its Justification 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 39\u003c\/p\u003e \u003cp\u003e2.2 Intuitive outline of the MI procedure 40\u003c\/p\u003e \u003cp\u003e2.3 The generic MI procedure 45\u003c\/p\u003e \u003cp\u003e2.4 Bayesian justification of mi 48\u003c\/p\u003e \u003cp\u003e2.5 Frequentist inference 50\u003c\/p\u003e \u003cp\u003e2.6 Choosing the number of imputations 55\u003c\/p\u003e \u003cp\u003e2.7 Some simple examples 56\u003c\/p\u003e \u003cp\u003e2.8 mi in more general settings 64\u003c\/p\u003e \u003cp\u003e2.9 Constructing congenial imputation models 72\u003c\/p\u003e \u003cp\u003e2.10 Discussion 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Multiple Imputation for Simple Data Structures 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multiple Imputation of Quantitative Data 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Regression imputation with a monotone missingness pattern 81\u003c\/p\u003e \u003cp\u003e3.2 Joint modelling 85\u003c\/p\u003e \u003cp\u003e3.3 Full conditional specification 90\u003c\/p\u003e \u003cp\u003e3.4 Full conditional specification versus joint modelling 92\u003c\/p\u003e \u003cp\u003e3.5 Software for multivariate normal imputation 93\u003c\/p\u003e \u003cp\u003e3.6 Discussion 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multiple Imputation of Binary and Ordinal Data 96\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Sequential imputation with monotone missingness pattern 96\u003c\/p\u003e \u003cp\u003e4.2 Joint modelling with the multivariate normal distribution 98\u003c\/p\u003e \u003cp\u003e4.3 Modelling binary data using latent normal variables 100\u003c\/p\u003e \u003cp\u003e4.4 General location model 108\u003c\/p\u003e \u003cp\u003e4.5 Full conditional specification 108\u003c\/p\u003e \u003cp\u003e4.6 Issues with over-fitting 110\u003c\/p\u003e \u003cp\u003e4.7 Pros and cons of the various approaches 114\u003c\/p\u003e \u003cp\u003e4.8 Software 116\u003c\/p\u003e \u003cp\u003e4.9 Discussion 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Imputation of Unordered Categorical Data 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Monotone missing data 119\u003c\/p\u003e \u003cp\u003e5.2 Multivariate normal imputation for categorical data 121\u003c\/p\u003e \u003cp\u003e5.3 Maximum indicant model 121\u003c\/p\u003e \u003cp\u003e5.4 General location model 125\u003c\/p\u003e \u003cp\u003e5.5 FCS with categorical data 128\u003c\/p\u003e \u003cp\u003e5.6 Perfect prediction issues with categorical data 130\u003c\/p\u003e \u003cp\u003e5.7 Software 130\u003c\/p\u003e \u003cp\u003e5.8 Discussion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Multiple Imputation in Practice 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Non-linear Relationships, Interactions, and Other Derived Variables 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 135\u003c\/p\u003e \u003cp\u003e6.2 No missing data in derived variables 141\u003c\/p\u003e \u003cp\u003e6.3 Simple methods 143\u003c\/p\u003e \u003cp\u003e6.4 Substantive-model-compatible imputation 152\u003c\/p\u003e \u003cp\u003e6.5 Returning to the problems 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Survival Data 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Missing covariates in time-to-event data 175\u003c\/p\u003e \u003cp\u003e7.2 Imputing censored event times 186\u003c\/p\u003e \u003cp\u003e7.3 Non-parametric, or 'hot deck' imputation 188\u003c\/p\u003e \u003cp\u003e7.4 Case–cohort designs 191\u003c\/p\u003e \u003cp\u003e7.5 Discussion 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Prognostic Models, Missing Data, and Multiple Imputation 200\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 200\u003c\/p\u003e \u003cp\u003e8.2 Motivating example 201\u003c\/p\u003e \u003cp\u003e8.3 Missing data at model implementation 201\u003c\/p\u003e \u003cp\u003e8.4 Multiple imputation for prognostic modelling 202\u003c\/p\u003e \u003cp\u003e8.5 Model building 202\u003c\/p\u003e \u003cp\u003e8.6 Model performance 204\u003c\/p\u003e \u003cp\u003e8.7 Model validation 206\u003c\/p\u003e \u003cp\u003e8.8 Incomplete data at implementation 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multi-level Multiple Imputation 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Multi-level imputation model 213\u003c\/p\u003e \u003cp\u003e9.2 MCMC algorithm for imputation model 224\u003c\/p\u003e \u003cp\u003e9.3 Extensions 231\u003c\/p\u003e \u003cp\u003e9.4 Other imputation methods 234\u003c\/p\u003e \u003cp\u003e9.5 Individual participant data meta-analysis 237\u003c\/p\u003e \u003cp\u003e9.6 Software 241\u003c\/p\u003e \u003cp\u003e9.7 Discussion 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Sensitivity Analysis: MI Unleashed 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Review of MNAR modelling 246\u003c\/p\u003e \u003cp\u003e10.2 Framing sensitivity analysis: estimands 249\u003c\/p\u003e \u003cp\u003e10.3 Pattern mixture modelling with mi 251\u003c\/p\u003e \u003cp\u003e10.4 Pattern mixture approach with longitudinal data via mi 263\u003c\/p\u003e \u003cp\u003e10.5 Reference based imputation 267\u003c\/p\u003e \u003cp\u003e10.6 Approximating a selection model by importance weighting 279\u003c\/p\u003e \u003cp\u003e10.7 Discussion 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multiple Imputation for Measurement Error and Misclassification 294\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 294\u003c\/p\u003e \u003cp\u003e11.2 Multiple imputation with validation data 296\u003c\/p\u003e \u003cp\u003e11.3 Multiple imputation with replication data 301\u003c\/p\u003e \u003cp\u003e11.4 External information on the measurement process 307\u003c\/p\u003e \u003cp\u003e11.5 Discussion 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multiple Imputation with Weights 312\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Using model-based predictions in strata 313\u003c\/p\u003e \u003cp\u003e12.2 Bias in the MI variance estimator 314\u003c\/p\u003e \u003cp\u003e12.3 MI with weights 317\u003c\/p\u003e \u003cp\u003e12.4 A multi-level approach 320\u003c\/p\u003e \u003cp\u003e12.5 Further topics 328\u003c\/p\u003e \u003cp\u003e12.6 Discussion 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multiple Imputation for Causal Inference 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Multiple imputation for causal inference in point exposure studies 333\u003c\/p\u003e \u003cp\u003e13.2 Multiple imputation and propensity scores 338\u003c\/p\u003e \u003cp\u003e13.3 Principal stratification via multiple imputation 343\u003c\/p\u003e \u003cp\u003e13.4 Multiple imputation for IV analysis 346\u003c\/p\u003e \u003cp\u003e13.5 Discussion 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Using Multiple Imputation in Practice 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A general approach 355\u003c\/p\u003e \u003cp\u003e14.2 Objections to multiple imputation 359\u003c\/p\u003e \u003cp\u003e14.3 Reporting of analyses with incomplete data 363\u003c\/p\u003e \u003cp\u003e14.4 Presenting incomplete baseline data 364\u003c\/p\u003e \u003cp\u003e14.5 Model diagnostics 365\u003c\/p\u003e \u003cp\u003e14.6 How many imputations? 366\u003c\/p\u003e \u003cp\u003e14.7 Multiple imputation for each substantive model, project, or dataset? 369\u003c\/p\u003e \u003cp\u003e14.8 Large datasets 370\u003c\/p\u003e \u003cp\u003e14.9 Multiple imputation and record linkage 375\u003c\/p\u003e \u003cp\u003e14.10 Setting random number seeds for multiple imputation analyses 377\u003c\/p\u003e \u003cp\u003e14.11 Simulation studies including multiple imputation 377\u003c\/p\u003e \u003cp\u003e14.12 Discussion 381\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Markov Chain Monte Carlo 384\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Metropolis Hastings sampler 385\u003c\/p\u003e \u003cp\u003eA.2 Gibbs sampler 386\u003c\/p\u003e \u003cp\u003eA.3 Missing data 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Probability Distributions 388\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Posterior for the multivariate normal distribution 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Overview of Multiple Imputation in R, Stata 394\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Basic multiple imputation using R 394\u003c\/p\u003e \u003cp\u003eC.2 Basic MI using Stata 395\u003c\/p\u003e \u003cp\u003eReferences 398\u003c\/p\u003e \u003cp\u003eAuthor Index 419\u003c\/p\u003e \u003cp\u003eIndex of Examples 429\u003c\/p\u003e \u003cp\u003eSubject Index 431\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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