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  • Gebundenes Buch

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…mehr

Produktbeschreibung
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.
  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 368
  • Erscheinungstermin: 8. Februar 2013
  • Englisch
  • Abmessung: 235mm x 157mm x 24mm
  • Gewicht: 685g
  • ISBN-13: 9780470740521
  • ISBN-10: 0470740523
  • Artikelnr.: 35454981
Inhaltsangabe
I Foundations 1 Introduction 1.1 Reasons for missing data 1.1.1 Patterns of missing data 1.1.2 Consequences of missing data 1.2 Inferential framework and notation 1.2.1 Missing Completely At Random (MCAR) 1.2.2 Missing At Random (MAR) 1.2.3 Missing Not At Random (MNAR) 1.2.4 Ignorability 1.3 Using observed data to inform assumptions about the missingness mechanism 1.4 Implications of missing data mechanisms for regression analyses 1.4.1 Partially observed response 1.4.2 Missing covariates 1.4.3 Missing covariates and response 1.4.4 Subtle issues I: the odds ratio 1.4.5 Implication for linear regression 1.4.6 Subtle issues II: sub sample ignorability 1.4.7 Summary: when restricting to complete records is valid 1.5 Summary 2 The Multiple Imputation Procedure and Its Justification 2.1 Introduction 2.2 Intuitive outline of the MI procedure 2.3 The generic MI Procedure 2.4 Bayesian justification of MI 2.5 Frequentist Inference 2.6 Choosing the number of imputations 2.7 Some simple examples 2.8 MI in More General Settings 2.8.1 Survey Sample Settings 2.9 Practical considerations for choosing imputation models 2.10 Discussion II Multiple imputation for cross sectional data 3 Multiple imputation of quantitative data 3.1 Regression imputation with a monotone missingness pattern 3.1.1 MAR mechanisms consistent with a monotone pattern 3.1.2 Justification 3.2 Joint modelling 3.2.1 Fitting the imputation model 3.3 Full conditional specification 3.3.1 Justification 3.4 Full conditional specification versus joint modelling 3.5 Software for multivariate normal imputation 3.6 Discussion 4 Multiple imputation of binary and ordinal data 4.1 Sequential imputation with monotone missingness pattern 4.2 Joint modelling with the multivariate normal distribution 4.3 Modelling binary data using latent normal variables 4.3.1 Latent normal model for ordinal data 4.4 General location model 4.5 Full conditional specification 4.5.1 Justification 4.6 Issues with over
fitting 4.7 Pros and cons of the various approaches 4.8 Software 4.9 Discussion 5 Imputation of unordered categorical data 5.1 Monotone missing data 5.2 Multivariate normal imputation for categorical data 5.3 Maximum indicant model 5.3.1 Continuous and categorical variable 5.3.2 Imputing missing data 5.3.3 More than one categorical variable 5.4 General location model 5.5 FCS with categorical data 5.6 Perfect prediction issues with categorical data 5.7 Software 5.8 Discussion 6 Non
linear relationships 6.1 Passive imputation 6.2 No missing data in non
linear relationships 6.3 Missing data in non
linear relationships 6.3.1 Predictive Mean Matching (PMM) 6.3.2 Just Another Variable (JAV) 6.3.3 Joint modelling approach 6.3.4 Extension to more general models and missing data pattern 6.3.5 Metropolis Hastings sampling 6.3.6 Rejection sampling 6.3.7 FCS approach 6.4 Discussion 7 Interactions 7.1 Interaction variables fully observed 7.2 Interactions of categorical variables 7.3 General non
linear relationships 7.4 Software 7.5 Discussion III Advanced Topics 8 Survival data, skips and large datasets 8.1 Time to event data 8.1.1 Imputing missing covariate values 8.1.2 Survival data as categorical 8.1.3 Imputing censored survival times 8.2 Non
parametric, or `hot deck' imputation 8.2.1 Non
parametric imputation for survival data 8.3 Multiple imputation for skips 8.4 Two
stage MI 8.5 Large datasets 8.5.1 Large datasets and joint modelling 8.5.2 Shrinkage by constraining parameters 8.5.3 Comparison of the two approaches 8.6 Multiple Imputation and record linkage 8.7 Measurement error 8.8 Multiple imputation for aggregated scores 8.9 Discussion 9 Multilevel multiple imputation 9.1 Multilevel imputation model 9.2 MCMC algorithm for imputation model 9.3 Imputing level 2 covariates using FCS 9.4 Individual patient meta
analysis 9.4.1 When to apply Rubin's rules 9.5 Extensions 9.5.1 Random level
1 covariance matrices 9.5.2 Model_t 9.6 Discussion 10 Sensitivity analysis: MI unleashed 10.1 Review of MNAR modelling 10.2 Framing sensitivity analysis 10.3 Pattern mixture modelling with MI 10.3.1 Missing covariates 10.3.2 Application to survival analysis 10.4 Pattern mixture approach with longitudinal data via MI 10.4.1 Change in slope post
deviation 10.5 Piecing together post
deviation distributions from other trial arms 10.6 Approximating a selection model by importance weighting 10.6.1 Algorithm for approximate sensitivity analysis by reweighting 10.7 Discussion 11 Including survey weights 11.1 Using model based predictions 11.2 Bias in the MI Variance Estimator 11.2.1 MI with weights 11.2.2 Estimation in Domains 11.3 A multilevel approach 11.4 Further developments 11.5 Discussion 12 Robust Multiple Imputation 12.1 Introduction 12.2 Theoretical background 12.2.1 Simple Estimating equations 12.2.2 The probability of missingness (POM) model 12.2.3 Augmented inverse probability weighted estimating equation 12.3 Robust Multiple Imputation 12.3.1 Univariate MAR missing data 12.3.2 Longitudinal MAR missing data 12.4 Simulation studies 12.4.1 Univariate MAR missing data 12.4.2 Longitudinal monotone MAR missing data 12.4.3 Longitudinal non
monotone MAR missing data 12.4.4 Non
longitudinal non
monotone MAR missing data 12.4.5 Conclusions 12.5 The RECORD study 12.6 Discussion Appendix A Markov Chain Monte Carlo Appendix B Probability distributions B.1 Posterior for the multivariate normal distribution Bibliography Index